Among metrological parameters, average temperature had the strongest correlation (rs = −0.675) with the cases. About 82% of Bangladeshi isolates had D614G at spike protein. Both temperature and UV index had strong effects on the frequency of mutations. Among host factors, coinfection is highly associated with frequency of different mutations. This study will give a complete picture of the effects of metrological parameters on COVID-19 cases, fatalities and mutation frequency that will help the authorities to take proper decisions.Coronavirus disease-2019 (COVID-19) has caused the recent pandemic worldwide. Research studies are focused on various factors affecting the pandemic to find effective vaccine or therapeutics against COVID-19. Environmental factors are the important regulators of COVID-19 pandemic. This study aims to determine the impact of weather on the COVID-19 cases, fatalities and frequency of mutations in Bangladesh. The impacts were determined on 1, 7 and 14 days of the case. The study was conducted based on Spearman's correlation coefficients. The highest correlation was found between population density and cases ( Coronaviridae) has triggered the coronavirus 2019 (COVID-19) pandemic worldwide [Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)  genome of ~30 000 bases , 2. The The main aim of this study is to analyse the correlation between metrological factors and frequency of mutations in SARS-CoV-2. The second objective of this study is to determine the relationship between host factors and SARS-CoV-2 mutation frequency. The third objective of this study is to investigate the correlation between environmental factors and COVID-19 pandemic in Bangladesh. This study will provide a better insight on the effects of environmental factors on COVID-19 pandemic in Bangladesh.2, Dhaka is the most populous capital in the world. The study period was from 07 March 2020 to 14 August 2020. On 07 March 2020, COVID-19 patients were detected for the first time in Bangladesh and considered as day 1 for the study.This study focused on the correlation of COVID-19 with metrological parameters in eight cities in Bangladesh. This study included Dhaka (23.71°N to 90.41°E), Chattogram (22°20′06″N to 91°49′57″E), Bogura (24°51′N to 89°22′E), Khulna (22°49′N to 89°33′E), Sylhet (24°54′N to 91°52′E) and Mymensingh (24°45′14″N to 90°24′11″E), Barishal (22°48′0″N to 90°30′0″E) and Rangpur (25°44′N to 89°15′E). With a population density of 46 997 person/kmhttps://covid19bd.idare.io/) and Institute of Epidemiology, Disease Control and Research (https://www.iedcr.gov.bd/website/) in Bangladesh, and cross-confirmed by analysing the data from official websites of WHO (www.who.int), Bing (www.bing.com/covid), Worldometers (www.worldometers.info/coronavirus/) and Johns Hopkins University (https://coronavirus.jhu.edu/). Environmental data including minimum temperature (⁰C), average temperature (°C), maximum temperature (°C), UV index, wind speed (km/h), rain fall (mm), relative humidity (%) were collected from different databases including official website of Bangladesh Meteorological Department (http://live4.bmd.gov.bd/satelite/v/sat_infrared/), meteoblue (www.meteoblue.com), AccuWeather (www.accuweather.com) and WeatherOnline (www.weatheronline.co.uk) during this study. The whole genome of SARS-CoV-2 isolated from Bangladesh and reference genome sequences were collected from the official website of GISAID (https://www.gisaid.org/). Accession number of sequences are: EPI_ISL_437912, EPI_ISL_445213, EPI_ISL_445214, EPI_ISL_445215, EPI_ISL_445216, EPI_ISL_445217, EPI_ISL_445244, EPI_ISL_447590, EPI_ISL_447897, EPI_ISL_447899, EPI_ISL_447904, EPI_ISL_450339, EPI_ISL_450340, EPI_ISL_450340, EPI_ISL_450341, EPI_ISL_450342, EPI_ISL_450343, EPI_ISL_450344, EPI_ISL_450345, EPI_ISL_450839, EPI_ISL_450840, EPI_ISL_450841, EPI_ISL_450842, EPI_ISL_450843, EPI_ISL_455420, EPI_ISL_455458, EPI_ISL_455459, EPI_ISL_458133, EPI_ISL_462090, EPI_ISL_462091, EPI_ISL_462092, EPI_ISL_462093, EPI_ISL_462094, EPI_ISL_462095, EPI_ISL_462096, EPI_ISL_462097, EPI_ISL_462098, EPI_ISL_464159, EPI_ISL_464160, EPI_ISL_464161, EPI_ISL_464162, EPI_ISL_464163, EPI_ISL_464164, EPI_ISL_466626, EPI_ISL_466627, EPI_ISL_466628, EPI_ISL_466629, EPI_ISL_466630, EPI_ISL_466636, EPI_ISL_466637, EPI_ISL_466638, EPI_ISL_466639, EPI_ISL_466644, EPI_ISL_466645, EPI_ISL_466649, EPI_ISL_466650, EPI_ISL_466686, EPI_ISL_466687, EPI_ISL_466688, EPI_ISL_466689, EPI_ISL_466690, EPI_ISL_466691, EPI_ISL_466692, EPI_ISL_466693, EPI_ISL_466694, EPI_ISL_468070-EPI_ISL_468078, EPI_ISL_469285, EPI_ISL_469286, EPI_ISL_469297-EPI_ISL_469300, EPI_ISL_470801, EPI_ISL_475083, EPI_ISL_475084, EPI_ISL_475165-EPI_ISL_475173, EPI_ISL_475238, EPI_ISL_475570, EPI_ISL_475571.The data of COVID-19 cases and fatalities were collected from official websites of the Directorate General of Health Services (DGHS) (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Multiple sequence alignment for the whole genome of Bangladeshi novel coronavirus strains and reference strains (Wuhan/WIV04/2019 and NC_045512/Wuhan-Hu-1) were conducted by using BioEdit 7.2.6 by using the ClustalW Multiple Alignment algorithm. Mutational analysis was performed for specific positions of novel coronavirus whole genome nucleotide sequences and peptide chains.The nucleotide sequences of the whole genome of novel coronaviruses were analysed using Chromas 2.6.5 . Sequence homology was determined by using the BLASTn program (rs) was determined between metrological parameters and COVID-19 cases and fatalities [rs) was determined among environmental factors, host factors and mutation frequency of novel coronaviruses. The association between two variables can be defined using a monotonic function by using Spearman's rank correlation coefficient (rs). The coefficient equation can be written asAll data were analysed using unbiased statistical approach. Spearman's rank correlation coefficient  had been reported from Dhaka followed by Chattogram (n = 15 775) Bogura (n = 5614), Khulna (n = 5039), Sylhet (n = 4912) and Mymensingh (n = 3078), Barishal (n = 2806) and Rangpur (n = 1736), respectively , followed by Dhaka (76%), Rangpur (74%) and Khulna (73%), respectively . The higUV is an important metrological factor that affects the transmission and mutation frequency of novel coronaviruses. During this study, the average UV index was recorded between 6.5 and 8 in Bangladesh. The highest average UV index was recorded in Barishal and Bogura  followedWind speed has direct effect on the spread of droplet nuclei containing virus particles. The average wind speed was recorded from 3 km/h to 19 km/h in this study. During this study, the highest average wind speed was recorded in Chattogram (17 km/h) followed by Bogura (16 km/h), Mymensingh (16 km/h), Khulna (15 km/h), Dhaka (14 km/h), Barishal (14 km/h), Rangpur (13 km/h) and Sylhet (11 km/h), respectively .Fig. 6.2) is the most populous city in the world with total population of 21 006 000, followed by Khulna (34 000 person/km2) with a total population of 1 122 000, Chattogram (19 800 person/km2) with a total population of 9 453 496, Sylhet (19 865 person/km2) with a total population of 526 412, Barishal (10 524 person/km2) with a total population of 385 093, Bogura (7763 person/km2) with a total population of 540 000, Mymensingh (5200 person/km2) with a total population of 476 543 and Rangpur (4167 person/km2) with a total population of 15 665 000, respectively.Dhaka (46 997 person/kmrs = −0.675), followed by average temperature 7 days ago (rs = −0.547), maximum temperature on the day (rs = −0.512) and minimum temperature on the day (rs = −0.486). Maximum temperature on the day had the highest correlation with total fatalities (rs = −0.611). The correlation for both COVID-19 cases and fatalities are negative, which indicates that at lower temperature the number of cases and fatalities increases.Spearman's correlation analysis between metrological and COVID-19 pandemic are presented in rs = 0.712) and fatalities (rs = 0.678) followed by the total population in every city.Second, relative humidity on the day had the greater correlation with the number of fatalities than case number. The correlation between relative humidity and COVID-19 pandemic reduces with increasing time span. Third, the association between UV index and the number of cases is the highest on the day of the case. Similarly, the correlation between UV index and the number of fatalities was the highest on the day. The correlation of relative humidity and UV index with COVID-19 pandemic was also negative. Fourth, among environmental factors, the average wind speed on the day had the highest correlation with the number of cases. The higher the wind speed was, the more the number of cases and fatalities were. Finally, the total population and population density of a city were highly correlated with the number of cases and fatalities in the city. The population density had the highest correlation with cases (rs = 0.611) with average temperature but frequency of rare mutation at spike protein had the highest correlation (rs = 0.658) with maximum temperature. Second, UV index was highly correlated with frequencies of all mutational events and the highest correlation was detected (rs = 0.678) with rare mutation at ORF1ab. Third, relative humidity had highest correlation (rs = 0.389) with frequency of D614G. Fourth, amount of rainfall was also strongly correlated with frequency of D614G. All of the metrological factors were positively related with the frequency of different mutations indicating increased temperature and UV index will favour origin of new mutations in the novel coronavirus. Finally, host factors-coinfection and gender variability were also positively correlated with mutation frequency. Coinfection had the highest association with common mutations at other structural proteins (rs = 0.671)  . The ageAfter determining the first genome of the novel coronavirus, thousands of mutational events had occurred. Some of the mutations had made the virus more persistent, environmental resistant and more deadly. The first 100 genomes of the novel coronavirus were analysed in this study. Mutations were detected throughout the whole genome. The most common mutation at ORF1ab region were P323L (NSP12) (88%) and I120F (NSP2) (72%) . The mosAmong 100 patients of this study, males were the predominant gender group (63%) followed by female patients (37%). Most of the cases (24%) were detected in the age group 30−39 years followed by 19% in 20−29 years and 18% in 40−49 years, respectively .Fig. 8.rs = −0.486); between maximum temperature and COVID-19 cases (rs = −0.512); between average temperature and COVID-9 cases (rs = −0.675) [et al. [rs = −0.586)/ fatalities (rs = −0.609) [With a moderate transmission rate, COVID-19 pandemic has transmitted throughout the entire world within very short time and continued to infect people , 31, 32. −0.675) , 34. Fou [et al.  did not  −0.609) , 34. Whirs value of COVID-19 cases in China [Previous studies have reported significant correlation of coronaviruses infection with metrological parameters , 35, 36.in China . Furtherin China , 33, 34.in China , 39. Thiin China , 39.rs = 0.687) between total population and COVID-19 cases in Turkey. However, this study includes both the total population and population density for COVID-19 cases and fatalities. This study reported stronger correlation between total population and cases (rs = 0.645) /fatalities (rs = 0.578); between population density and cases (rs = 0.712) /fatalities (rs = 0.678) than previous study [Şahin  detectedus study .Mutational events are most important aspects for novel coronavirus infection, transmission and persistence in the host cells. Mutations at spike proteins can create difficulties to develop effective vaccine or therapeutics against novel coronaviruses , 29, 30.rs = 0.654), rare mutation (rs = 0.598) and at S- D614G (rs = 0.611); between maximum temperature and rare mutation at S (rs = 0.658). Among other metrological factors, UV index had the highest correlation with frequency of mutations at every site in the genome. Among host factors, coinfections had strong correlation with frequency of mutations at ORF1ab- common mutation (rs = 0.485), rare mutation (rs = 0.642), at S-D614G (rs = 0.644) and common mutation at other protein (rs = 0.671). Both age and gender had significant correlation with frequency of mutations at many sites of the genome. As of 18 August 2020 no other study has described the correlation between metrological parameters/host factors and frequency of mutations in novel coronaviruses. However, for other viruses like influenza and Newcastle disease viruses there are reports of environmental and host factors association with the virus mutations [This study determined the correlation between metrological parameters and mutational frequency of novel coronaviruses for the first time. This study included both metrological parameters and host factors to determine correlation with frequency of mutations. Studies have found significant effects of environmental factors and UV radiation on the frequency of mutation of influenza virus and Newcastle disease virus , 43. Thi rs = 0.6, at S-D6This study has described the highest correlation between metrological parameters and COVID-19 cases; strong correlation between metrological parameters and COVID-19 fatalities; first time correlation between metrological factors and frequency of mutations in novel coronaviruses; first time correlation between host factors and frequency of mutation. This study also provided the mutation frequency at different sites of novel coronaviruses. A complete picture on the effect of metrological parameters on COVID-19 pandemic and novel coronavirus mutation frequency has been depicted in this study. This study will work as a baseline for the future studies focusing on the environmental and host factors affecting the COVID-19 pandemic. Further, this study will work as a guideline for future studies by containing important information on environmental and host factors impact on mutations of coronaviruses.Other factors like duration of lockdown, mobility of huge number of workers, social and religious gatherings, not using masks, movement of people during vacations, lack of proper detection of COVID-19 patients etc. are significantly affecting the pandemic. If direct contact is not avoided, social distance and personal hygiene are not maintained, environmental factors cannot control the COVID-19 pandemic alone. This study describes high frequency of common and rare mutations in the novel coronavirus genome in Bangladesh. Circulation of these mutants will certainly increase the duration of the pandemic and reduce the effectiveness of vaccine or therapeutics in future. The main limitation of this study is the variation of case number. The actual case and fatality number may vary slightly due to the lack of complete diagnosis of the population. In future, studies including more isolates of viruses, large number of clinical data and environmental data of longer periods can predict more accurate effects of various factors on COVID-19.To the best of our knowledge, this is the first study reporting correlation of environmental factors with COVID-19 pandemic in three time frames in Bangladesh with temperature about 27 °C. The strongest correlations between metrological factors and COVID-19 cases/fatalities were specified on the day of cases/fatalities. The highest correlation was detected between population density and cases followed by total population and cases indicating the mobility and crowd are actively increasing the cases and fatalities. For the first time, this study describes the effects of metrological parameters on the frequency of mutation at different sites in novel coronavirus. Both temperature and UV index had strong effects on different mutation events. Among host factors, coinfection also affected different mutations strongly. Including COVID-19 cases, fatalities, mutations, mutation frequency and clinical data this study provides a complete picture of the COVID-19 pandemics and environmental effects on this pandemic. This study will provide useful implications about COVID-19 pandemic and both for policy makers and public to take decision to reduce the health burden of this outbreak.


High-throughput RNA-seq enables comprehensive analysis of the transcriptome for various purposes. However, this technology generally generates massive amounts of sequencing reads with a shorter read length. Consequently, fast, accurate, and flexible tools are needed for assembling raw RNA-seq data into full-length transcripts and quantifying their expression levels. In this protocol, we report TransBorrow, a novel transcriptome assembly software specifically designed for short RNA-seq reads. TransBorrow is employed in conjunction with a splice-aware alignment tool (e.g. Hisat2 and Star) and some other transcriptome assembly tools . The protocol encompasses all necessary steps, starting from downloading and processing raw sequencing data to assembling the full-length transcripts and quantifying their expressed abundances. The execution time of the protocol may vary depending on the sizes of processed datasets and computational platforms. First, create a subdirectory “Star” in your directory and enter it. Next, employ the “wget” command to download the most recent package from the appropriate downloading link address. Subsequently, extract the contents of the downloaded package to obtain the executable file. Lastly, add the path of the Star binary to a directory encompassed within your system’s PATH variable.$ mkdir Star$ cd Starhttps://github.com/alexdobin/STAR/archive/2.5.3a.tar.gz$ wget $ tar -xzf 2.5.3a.tar.gz$ cd STAR-2.5.3a$ echo “export PATH=$PATH:/mnt/data0/zhaody/Star/STAR-2.5.3a”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing Star is shown as follows.Samtools is a software package designed for processing SAM and BAM format files, which offers a diverse range of command-line utilities for manipulating, converting, and analyzing SAM/BAM files. It enables various tasks such as file format conversion (e.g. SAM to BAM conversion), sorting and indexing SAM/BAM files, assessing coverage and depth of aligned loci, extracting sequences from specific regions, and more .To install Samtools, the following steps can be undertaken. First, create a subdirectory named “Samtools” within your directory. Subsequently, navigate to the “Samtools” directory and use the “wget” command to download the latest package from the appropriate download link address. Afterwards, extract the downloaded package and configure the environment variables for Samtools accordingly. Finally, add the Samtools binary directory to the PATH environment variable in the shell configuration file (see the following illustration)An illustration of installing samtools is shown as follows.$ mkdir Samtools$ cd Samtoolshttps://nchc.dl.sourceforge.net/project/samtools/samtools/1.17/samtools-1.17.tar.bz2$ wget $ tar jxvf samtools-1.17.tar.bz2$ cd samtools-1.17$ ./configure—prefix=/mnt/data0/zhaody/Samtools/samtools-1.17$ make$ make install$ echo “export PATH=$PATH:/mnt/data0/zhaody/Samtools/samtools-1.17”≫∼/.bashrc$ source ∼/.bashrcAfter installing it, you can type the command “samtools” to see the help documentation information.StringTie is an efficient transcriptome assembler, which iteratively extracts the heaviest path from a splice graph, and then estimates the abundance via a network flow algorithm [12].To install StringTie, the following steps can be followed. First, create a subdirectory named “StringTie” within your directory. Next, navigate to the “StringTie” directory and utilize the “wget” command to download the latest package from the appropriate download link address. After downloading, extract the contents of the package to obtain the executable file. Finally, add the path of the StringTie binary directory to the PATH environment variable in the shell configuration file.$ mkdir StringTie$ cd StringTiehttp://ccb.jhu.edu/software/stringtie/dl/stringtie-2.2.1.Linux_x86_64.tar.gz$ wget $ tar -xzvf stringtie-2.2.1.Linux_x86_64.tar.gz$ echo “export PATH=$PATH:/mnt/data0/zhaody/Stringtie/stringtie-2.2.1.Linux_x86_64”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing StringTie is shown as follows.If you have successfully installed it, you can type the command “stringtie -h” to see the help documentation information.Cufflinks is a software tool specifically designed for analyzing RNA-seq data. It constructs the overlap graph model based on the fragment alignments, and applies a minimum path cover model to search for the transcript-representing paths .To install Cufflinks, the following steps can be undertaken: First, create a subdirectory named “Cufflinks” within your directory. Next, navigate to the “Cufflinks” directory and use the command “wget” to download the latest package from the appropriate download link address. After downloading the package, extract its contents to obtain the executable file. Finally, add the Cufflinks binary directory to the PATH environment variable in the shell configuration file.$ mkdir Cufflinks$ cd Cufflinkshttp://cole-trapnell-lab.github.io/cufflinks/assets/downloads/cufflinks-2.2.1.Linux_x86_64.tar.gz$ wget $ tar -xzvf cufflinks-2.2.1.Linux_x86_64.tar.gz$ echo “export PATH=$PATH:/mnt/data0/zhaody/Cufflinks/cufflinks-2.2.1.Linux_x86_64”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing Cufflinks is shown as follows.If you have successfully installed it, you can type the command “cufflinks -h” to see the help documentation information.Scallop is a highly efficient transcriptome assembly software designed for the reconstruction of transcripts from RNA-Seq data. It was built upon the standard paradigm of the splice graph, and it decomposed the graphs through optimizing several competing objectives while preserving long-range phasing paths .To install Scallop, the following steps can be followed. First, create a subdirectory named “Scallop” within the main directory. Next, navigate to the “Scallop” directory and utilize the command “wget” to download the latest package from the appropriate download link address. After downloading, extract the contents of the package to obtain the executable file. Finally, add the Scallop binary directory to the PATH environment variable in the shell configuration file.$ mkdir Scallop$ cd Scallophttps://github.com/Kingsford-Group/scallop/releases/download/v0.10.5/scallop-0.10.5_linux_x86_64$ wget .tar.gz$ tar -xzvf scallop-0.10.5_linux_x86_64.tar.gz$ echo “export PATH=$PATH:/mnt/data0/zhaody/Scallop/scallop-0.10.5_linux_x86_64”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing Scallop is shown as follows.If you have successfully installed it, you can type the command “scallop -h” to see the help documentation information.boost directory and run “./bootstrap.sh”. Finally, type “./b2 install—prefix=<YOUR_BOOST_INSTALL_DIRECTORY>” to install Boost .The Boost library is a highly regarded, portable, and open-source C++ library that is essential for TransBorrow. To install Boost, follow these steps. First, create a subdirectory named “Boost” under your directory. Next, navigate into the “Boost” directory and use the command “wget” to download the latest package from the appropriate download link address. Then, unzip the downloaded package and change to the Please note that the version number and URL provided in your original text may need to be updated. Make sure to use the latest version of Boost and adjust the URL accordingly.$ mkdir Boost$ cd Boosthttps://boostorg.jfrog.io/artifactory/main/release/1.82.0/source/boost_1_82_0.tar.gz$ wget $ tar -xzvf boost_1_82_0.tar.gz$ cd boost_1_82_0$ ./bootstrap.sh$ ./b2 install—prefix=/mnt/data0/zhaody/BoostAn illustration of installing Boost is shown as follows.If the Boost is installed successfully, you can see the “lib” and “include” directories in YOUR_BOOST_INSTALL_DIRECTORY directory. Take note of the Boost installation directory, because you need to tell the TransBorrow installer where to find Boost later on.TransBorrow is an accurate and efficient transcriptome algorithm, which borrows the assemblies from different assemblers to search for reliable subsequences by building a colored graph from those borrowed assemblies, and employs a newly designed path extension strategy to accurately search for a transcript-representing path cover over each splicing graph .To install TransBorrow, create a subdirectory named TransBorrow under the main directory. After entering the directory, use the command “wget” to download a package from the appropriate download link address and unzip it. In addition, to configure the environment of TransBorrow, change to the “bamtools” directory and make a new directory named “build”, then type “cmake” and “make” to make it install.$ mkdir TransBorrow$ cd TransBorrowhttps://sourceforge.net/projects/transcriptomeassembly/files/TransBorrow/TransBorrow_v.1.3.tar.gz$ wget $ tar -xzvf TransBorrow_v.1.3.tar.gz$ cd TransBorrow_v.1.3$ cd bamtools$ mkdir build$ cd build$ cmake ./$ make$ cd ./An illustration of building bamtools is shown as follows.Assuming the build process finished correctly, you should be able to find the toolkit executable in directory “./bin/”, the Bamtools API Utils libraries “./lib/”, and the Bamtools API headers “./include/”.lib and include directories (absolute path) of both Boost and bamtools to the CMakeLists.txt file located in TransBorrow_v.1.3/src/to configure the installation environment for TransBorrow (see the following illustration for details).Then, add the Please note that the directory structure and file names may vary depending on your specific setup. Adjust the paths and commands accordingly.$ cd src$ vim CMakeList.txtset(BOOST_LIB_DIR/mnt/data0/zhaody/Boost/lib)#setset(BOOST_INCLUDE_DIR/mnt/data0/zhaody/Boost/include)#setset(BAMTOOLS_LIB_DIR/mnt/data0/zhaody/TransBorrow/TransBorrow_v.1.3/bamtools/lib)#set(BAMTOOLS_LIB_DIR/storage/juntaosdu/yuting/bamtools/lib)set(BAMTOOLS_INCLUDE_DIR/mnt/data0/zhaody/TransBorrow/TransBorrow_v.1.3/bamtools/include)#set(BAMTOOLS_INCLUDE_DIR/storage/juntaosdu/yuting/bamtools/include)An illustration of setting the installation environment for TransBorrow is shown as follows.Change to the TransBorrow root directory and make a new directory named “build” and change into it, then type “cmake ./src” and “make” commands for the final installation of TransBorrow.$ cd ../$ mkdir build$ cd build$ cmake ./src$ make$ cd ./src$ makeAn illustration of building TransBorrow is shown as follows.$ cd TransBorrow$ echo “export PATH=$PATH:/mnt/data0/zhaody/TransBorrow/TransBorrow_v.1.3/build”≫∼/.bashrc$ source ∼/.bashrcFinally, add the TransBorrow directory to the PATH environment variable in the shell configuration file. See the following illustration.If you have successfully installed it, you can type the command “TransBorrow -h” to see the help documentation information see .The Gffcompare software is a powerful tool utilized for comparing, merging, annotating, and estimating the accuracy of one or more GFF/GTF files in comparison to reference annotations . To inst$ mkdir Gffcompare$ cd Gffcomparehttp://ccb.jhu.edu/software/stringtie/dl/gffcompare-0.12.6.Linux_x86_64.tar.gz$ wget $ tar -xzvf gffcompare-0.12.6.Linux_x86_64.tar.gz$ echo “export PATH=$PATH:/mnt/data0/zhaody/Gffcompare/gffcompare-0.12.6.Linux_x86_6”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing Gffcompare is shown as follows.If you have successfully installed it, you can type the command “gffcompare -h” to see the help documentation information.TACO is a tool to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO accepts as input a set of GTF files containing transcripts assembled from individual libraries, and it employs a dynamic programming path search strategy in the path graph to reconstruct the transcripts. To install TACO, the following steps should be followed. First, create a subdirectory named “TACO” within the main directory. Subsequently, navigate into the “TACO” folder and utilize the “wget” command to download the latest package from the appropriate download link address. Next, extract the downloaded package to acquire the executable file. Finally, add the TACO binary directory to the PATH environment variable.$ mkdir TACO$ cd TACOhttps://github.com/tacorna/taco/releases/download/v0.7.3/taco-v0.7.3.Linux_x86_64.tar.gz$ wget wget $ tar -xzvf taco-v0.7.3.Linux_x86_64.tar.gz$ echo “export PATH=$PATH:/mnt/data0/zhaody/TACO/taco-v0.7.3.Linux_x86_64”≫∼/.bashrc$ source ∼/.bashrcAn illustration of installing TACO is shown as follows.The RNA-seq data can be downloaded from databases such as the NCBI or the European Bioinformatics Institute (EBI), etc. Alternatively, the researchers can use the RNA-seq data sequenced by themselves. Note that the file format of the input sequencing data must be in FASTA or FASTQ format.RNA-seq technology is mainly categorized into single-end sequencing and paired-end sequencing, based on differences in DNA library preparation during the sequencing process. Single-end sequencing means sequencing only one end of the target DNA/RNA, which requires less sequencing time and cost. In comparison, paired-end sequencing refers to sequencing both ends of the target DNA/RNA, which improves the accuracy of mapping and assembly . AdditioTherefore, the proposed protocol utilized three human RNA-seq data sets based on different types of RNA-seq technology see  to demonAn illustration of downloading sequencing data is shown as follows.$ cd Sratoolkit$ prefetch SRR7807492$ cd SRR7807492$ fastq-dump—split-3 SRR7807492.sra$ prefetch ERR3639851$ cd ERR3639851$ fastq-dump—split-3 ERR3639851.sra$ prefetch SRR10611964$ cd SRR10611964$ fastq-dump—split-3 SRR10611964.sra$ mkdir ref_genome$ cd ref_genomehttp://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz$ wget $ gunzip *.gz$ cat *.fa > ref_genome.fa$ rm chr*.faAfter obtaining raw RNA-seq reads, download the human reference genome (the version used in this protocol was hg19) as follows. Create a file named “ref_genome” in your home directory and utilize the “wget” command with the appropriate download link address to download the corresponding file. If you complete the downloading, extract the file (see the following illustration for details).The human genome is about 3GB and ensures that you have sufficient storage capacity to accommodate both the downloaded and uncompressed files.In this protocol, all the reference transcripts were set as the ground truth to evaluate the performance of the assemblers. To acquire the reference transcripts, navigate to the “ref_genome” directory and employ the “wget” command along with the appropriate downloading link address (see the following illustration for details).$ cd ref_genomehttps://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/GRCh37_mapping/gencode.v44lift37.annotation.gtf.gz$ wget $ gunzip gencode.v44lift37.annotation.gtf.gzAn illustration of downloading the reference transcriptome is shown below.Upon successful downloading, you will find three folders named SRR7807492, ERR3639851, and SRR10611964 within the Sratoolkit directory, which store the RNA-seq data in the “.fastq” format  The “hisat2-build” command generates the reference genome index and is typically followed by the reference genome file in “.fa” format and the desired output file name. The recommended command for this task is as follows.# hisat2-build ref_genome.fa ref_index_genome,ref_genome.fa is the reference genome.where Besides, when utilizing a transcriptome annotation library to aid in building the reference genome index, “hisat2-build” requires the use of the “—ss” parameter, which specifies the splice site information of the reference transcripts, and the “—exon” parameter, which denotes the exon information of the reference transcripts. The running command should be as follows.# hisat2-build—ss genome.ss—exon genome.exon ref_genome.fa ref_index_genome,genome.ss and genome.exon can be extracted from a transcriptome annotation file (in GTF format) using the script “extract_splice_sites.py” and “extract_exons.py” provided in the Hisat2 package.where 2) The command “hisat2” is utilized for aligning RNA-seq reads to the reference genome. The recommended command is as follows.# hisat2 -p 8—dta -x ref_index_genome -1 reads_1.fastq -2 reads_2.fastq -S test_genome.sam where the parameter “-p” specifies the number of threads running, typically set to be equal to or slightly fewer than the number of available CPU cores. The parameter “—dta” means reporting alignments tailored for transcript assemblers. The parameters “-x” and “-S” provide the indexed reference genome and the name of the output SAM file, respectively. The parameters “-1” and “-2” specify the two paired-end sequencing files in FASTQ format, reads_1.fastq and reads_2.fastq, respectively. If the sequencing data is single-ended, the parameter “-U” is used with the corresponding FASTQ file.The first step of using Hisat2 is to construct an index for the reference genome. Begin by navigating to the Hisat2 directory and employing the “mv” command to relocate the reference genome file from the “ref_genome” directory to the current directory. Subsequently, utilize the “hisat2-build” command to generate an index for the reference genome. This process will result in the creation of eight files with the “.ht2” suffix. This process of building the index typically takes approximately half an hour (see the following illustration for details).$ cd Hisat2$ mv/mnt/data0/zhaody/ref_genome/ref_genome.fa.$ hisat2-build ref_genome.fa ref_index_genomeAn illustration of the Hisat2 building index is shown below.It is noteworthy that incorporating the annotation information of the transcripts while building the genome index can improve the accuracy of Hisat2 alignment. Utilizing this approach is also an option as follows. Navigate to the Hisat2 directory and employ the “mv” command to relocate the reference genome file and the transcript annotation file from the “ref_genome” directory to the current directory. Then, extract splice-site and exon information from the annotation. Proceed by using the “hisat2-build” command to construct an index for the reference genome. Finally, eight files will be generated with the “.ht2” suffix. This process of building the index typically takes approximately one hour (see the following illustration for details).$ cd Hisat2$ mv/mnt/data0/zhaody/ref_genome/ref_genome.fa.$ mv/mnt/data0/zhaody/ref_genome/gencode.v44lift37.annotation.gtf.$ extract_splice_sites.py gencode.v44lift37.annotation.gtf >genome.ss$ extract_exons.py gencode.v44lift37.annotation.gtf >genome.exon$ hisat2-build—ss genome.ss—exon genome.exon ref_genome.fa ref_index_genomeAn illustration of the Hisat2 building index using the annotation file is shown below.To align the raw RNA-seq reads to the reference genome, first, navigate to the Hisat2 directory. Then, utilize the “mv” command to relocate the individual “.fastq” files of the three sample datasets from the “Sratoolkit” directory to the current directory. Afterward, use the “hisat2” command to align the RNA-seq reads from the three sample datasets to the reference genome. Upon completion of the alignment process, you will obtain three mapping files in “.sam” format, with each file corresponding to one of the sample datasets  The command “STAR—runMode genomeGenerate” is utilized for constructing the reference genome index. The recommended command is as follows.# STAR—runThreadN 8—runMode genomeGenerate—genomeDir ./index—genomeFastaFiles ref_genome.fa—sjdbGTFfile reference_Transcripts.gtf—sjdbOverhang readlenth-1where the parameter “—runThreadN” denotes the number of running threads, typically set to be equal to or slightly fewer than the number of CPU cores available. The parameter “—genomeDir” specifies the output directory where the index file is stored. The parameters “—genomeFastaFiles” and “—sjdbGTFfile” indicate the reference genome and reference transcripts respectively. It is generally recommended to set the parameter “—sjdbOverhang” to be the length of sequencing reads minus one.2) The command “STAR” is employed for aligning RNA-seq reads to the reference genome. The recommended command is as follows:# STAR—runThreadN 8—genomeDir ./index—readFilesIn reads_1.fastq reads_2.fastq—outSAMtype BAM SortedByCoordinatewhere the parameter “—runThreadN” indicates the number of threads running, typically set to be equal to or slightly fewer than the number of available CPU cores. The parameter “—genomeDir” specifies the output directory where the index file is stored. The parameter “—readFilesIn” provides the sequencing reads in FASTQ format, namely reads_1.fastq and reads_2.fastq. The parameter “—outSAMtype BAM SortedByCoordinate” is used to generate a sorted BAM file as the final mapping output, eliminating the need for using Samtools to convert the SAM file to BAM. By default, the final mapping file is saved in the current folder.The first step of using Star is to construct an index for the reference genome. Beginning by entering the Star directory and employing the “mv” command to relocate the reference genome file and the transcript annotation file from the “ref_genome” directory to the current directory. Subsequently, utilize the “STAR” command to generate an index for the reference genome. (see the following illustration for details).$ cd Star$ mv/mnt/data0/zhaody/ref_genome/ref_genome.fa.$ mv/mnt/data0/zhaody/ref_genome/gencode.v44lift37.annotation.gtf.$ STAR—runThreadN 8—runMode genomeGenerate—genomeDir ./index—genomeFastaFiles ref_genome.fa—sjdbGTFfile gencode.v44lift37.annotation.gtf—sjdbOverhang 99An illustration of the Star building index is shown below.To align the raw RNA-seq reads to the reference genome with Star, please navigate to the STAR directory. Then, utilize the “mv” command to relocate the individual “.fastq” files of the three sample datasets from the “Sratoolkit” directory to the current directory. Afterward, use the “STAR” command to align the RNA-seq reads from the three sample datasets to the reference genome. Upon completion of the alignment process, you will obtain three mapping files in “BAM” format, with each file corresponding to one of the sample datasets. By default, the final mapping file is saved in the current folder.$ cd STAR$ mv/mnt/data0/zhaody/Sratoolkit/SRR7807492/SRR7807492_1.fastq.$ mv/mnt/data0/zhaody/Sratoolkit/SRR7807492/SRR7807492_2.fastq.$ mv/mnt/data0/zhaody/Sratoolkit/ERR3639851/ERR3639851.fastq.$ mv/mnt/data0/zhaody/Sratoolkit/SRR10611964/SRR10611964_1.fastq.$ mv/mnt/data0/zhaody/Sratoolkit/SRR10611964/SRR10611964_2.fastq.$ STAR—runThreadN 8—genomeDir ./index—readFilesIn SRR7807492_1.fastq SRR7807492_2.fastq—outSAMtype BAM SortedByCoordinate$ STAR—runThreadN 8—genomeDir ./index—readFilesIn ERR3639851.fastq—outSAMtype BAM SortedByCoordinate$ STAR—runThreadN 8—genomeDir ./index—readFilesIn SRR10611964_1.fastq SRR10611964_2.fastq—outSAMtype BAM SortedByCoordinateAn illustration of Star mapping is shown below.TransBorrow performs transcript assembly by utilizing the alignment of RNA-seq reads to the reference genome, as well as the assemblies generated from other assemblers . In this protocol, we utilize the read alignments generated by Hisat2 to illustrate the entire TransBorrow’s transcript assembly process.# TransBorrow [options] -r <combined_assemblies> -g <reference_genome> -b <bam_file> -s <strandness>It is worth noting that during the TransBorrow assembly process, specific parameters and commands need to be selected according to the data types and your needs to achieve optimal assembly results. The followings are some important parameter descriptions and suggestions for using TransBorrow to assemble transcripts.According to data types, you need to choose the appropriate parameter “-s”, e.g. for the data SRR7807492 that is paired-end and nonstranded, adjust the parameter to “-s unstranded”.The parameter “-c” indicates the minimum coverage of recovered transcripts, which helps to filter out potentially low-confidence transcripts.The parameter “-l” indicates the minimum length (bp) of recovered transcripts. This parameter helps to filter out short transcripts.The parameter “-d” refers to the minimum seed coverage in the path-extensions procedure of the TransBorrow algorithm. The default value for this parameter is typically set to 0. A higher value of min_seed for extension will result in more stringent filtering, potentially excluding low-coverage regions and leading to fewer but more confident assembled transcripts.To run StringTie, begin by navigating to the StringTie directory and moving the three mapping files in BAM format to the current directory. Then, execute the “stringtie” command to conduct the transcript assembly process, the assembled transcripts will be generated in GTF format (see the following illustration). Moreover, it is essential to adjust the parameters according to the provided data types. The recommended command using StringTie is as follows.# stringtie -p 8 -o OutputFile.gtf InputFile.bamIn this command, “-p” specifies the number of threads to be used, which is typically set to be equal to or slightly fewer than the number of available CPU cores. The “-o” parameter specifies the name of the output GTF file, and InputFile.bam indicates the name of the input BAM file. The resulting assembly GTF file will be saved in the current folder.$ cd Stringtie$ mv/mnt/data0/zhaody/Samtools/SRR7807492_genome.bam.$ mv/mnt/data0/zhaody/Samtools/ERR3639851_genome.bam.$ mv/mnt/data0/zhaody/Samtools/SRR10611964_genome.bam.$ stringtie -p 8 -o SRR7807492_genome_stringtie.gtf SRR7807492_genome.bam$ stringtie -p 8 -o ERR3639851_genome_stringtie.gtf ERR3639851_genome.bam$ stringtie -p 8 -o SRR10611964_genome_stringtie.gtf SRR10611964_genome.bamAn illustration of running StringTie with its default sets is shown below.To assemble the transcripts by Cufflinks, navigate to the Cufflinks directory and move the three mapping files in BAM format to the current directory. Subsequently, execute the “cufflinks” command to carry out the transcript assembly, which will generate three GTF files containing the assembled transcripts. It is imperative to adjust the commands accordingly for the provided data types (see the following illustration). The recommended command using Cufflinks is as follows.# cufflinks -p 8 -o OutputFile.gtf InputFile.bam—library-type fr-firststrandIn this command, the “-p” parameter specifies the number of threads to be used, typically matching or slightly lower than the available CPU cores. The “-o” parameter is followed by the desired name for the output GTF file and the name of the input BAM file. The “—library-type fr-firststrand” parameter indicates that the input RNA-seq data were generated using the first-strand cDNA synthesis method. Conversely, the “—library-type rf-secondstrand” parameter would indicate that the input RNA-seq data was generated using the second-strand cDNA synthesis method. The default assumption for input RNA-seq data is nonstranded.$ cd Cufflinks$ mv/mnt/data0/zhaody/Stringtie/SRR7807492_genome.bam.$ mv/mnt/data0/zhaody/Stringtie/ERR3639851_genome.bam.$ mv/mnt/data0/zhaody/Stringtie/SRR10611964_genome.bam.$ cufflinks -p 8 -o SRR7807492_genome_cufflinks.gtf SRR7807492_genome.bam$ cufflinks -p 8 -o ERR3639851_genome_cufflinks.gtf ERR3639851_genome.bam$ cufflinks -p 8 -o SRR10611964_genome_cufflinks.gtf SRR10611964_genome.bam—library-type fr-firststrandAn illustration of running Cufflinks with its default sets is shown below.To assemble the transcripts by Scallop, begin by navigating to the Scallop directory and moving the three mapping files in BAM format to the current directory. Next, execute the “scallop” command to perform transcript assembly, and the assembled transcripts will be generated in GTF format. It is important to note that Scallop requires different parameters to be configured based on the specific data type of the input. Therefore, it is necessary to adjust the commands accordingly for the provided data types. The recommended command using Scallop is as follows.# scallop -i InputFile.bam -o OutputFile.gtf—library-type firstIn this command, the “-i” parameter is followed by the name of the input BAM file, and the “-o” parameter is followed by the desired name for the output GTF file. The “—library-type first” parameter indicates that the input RNA-seq data were generated using the first-strand cDNA synthesis method. Conversely, the “—library-type second” parameter would indicate that the input RNA-seq data were generated using the second-strand cDNA synthesis method. Additionally, the “—library-type unstranded” parameter indicates that the input RNA-seq data are nonstranded. By default, the final assembly GTF file will be saved in the current directory.$ cd Scallop$ mv/mnt/data0/zhaody/Cufflinks/SRR7807492_genome.bam.$ mv/mnt/data0/zhaody/Cufflinks/ERR3639851_genome.bam.$ mv/mnt/data0/zhaody/Cufflinks/SRR10611964_genome.bam.$ scallop -i SRR7807492_genome.bam -o SRR7807492_genome_scallop.gtf—library_type unstranded$ scallop -i ERR3639851_genome.bam -o ERR3639851_genome_scallop.gtf—library_type unstranded$ scallop -i SRR10611964_genome.bam -o SRR10611964_genome_scallop.gtf—library_type firstAn illustration of running Scallop with its default sets is shown below.Besides the alignment file, TransBorrow needs to take as input the transcripts assembled by different transcript assembly tools, which should be merged together first. Create a new directory named “Gtf_file” in the home directory to store the merged files. Next, move the nine GTF files to the current directory. Finally, utilize the cat command to merge the three GTF files corresponding to each sample into one file (see the following illustration).$ mkdir Gtf_file$ cd Gtf_file$ mv/mnt/data0/zhaody/Stringtie/SRR7807492_genome_stringtie.gtf.$ mv/mnt/data0/zhaody/Cufflinks/SRR7807492_genome_cufflinks.gtf.$ mv/mnt/data0/zhaody/Scallop/SRR7807492_genome_scallop.gtf.$ cat SRR7807492_genome_stringtie.gtf SRR7807492_genome_cufflinks.gtfSRR7807492_genome_scallop.gtf > combine_SRR7807492.gtfAn illustration of merging the assembled transcripts of different assemblers is shown as follows.To utilize TransBorrow, navigate to the TransBorrow directory and move the mapping BAM files, reference genome FASTA file, and the merged transcripts for each sample to the current directory. Employ the “TransBorrow” command to perform the transcript assembly procedure (see the following illustration for details). It is important to consider that different parameters need to be configured for different types of sample data.The recommended command using TransBorrow is as follows.# TransBorrow -r CombinedInputFile.gtf -b InputFile.bam -g ref_genome.fa -s first -o OutputDirctoryIn this command, the “-r” parameter is followed by the combined transcripts assembled by different tools in GTF format. The “-b” parameter is followed by the name of the input BAM file. The “-g” parameter is followed by the reference genome in FASTA format. The parameter “-s” indicates the type of sequencing data, whether it is stranded or nonstranded, single-ended or paired-ended. In this case, “first” indicates that the sequencing data were generated using the first-strand cDNA synthesis method. The “-o” parameter is followed by a directory that stores the temporary files and the final assembled GTF file. Please refer to $ cd TransBorrow$ mv/mnt/data0/zhaody/Gtf_film/combine_SRR7807492.gtf.$ mv/mnt/data0/zhaody/Gtf_film/combine_ERR3639851.gtf.$ mv/mnt/data0/zhaody/Gtf_film/combine_SRR10611964.gtf.$ mv/mnt/data0/zhaody/Scallop/SRR7807492_genome.bam.$ mv/mnt/data0/zhaody/Scallop/ERR3639851_genome.bam.$ mv/mnt/data0/zhaody/Scallop/SRR10611964_genome.bam.$ mv/mnt/data0/zhaody/Hisat2/ref_index_genome.$ TransBorrow -r combine_SRR7807492.gtf -b SRR7807492_genome.bam -g ref_genome.fa -s unstranded -o ./TransBorrow_results/SRR7807492_TransBorrow.gtf -n 3 -t 8$ TransBorrow -r combine_ERR3639851.gtf -b ERR3639851_genome.bam -g ref_genome.fa -s single_unstranded -o ./TransBorrow_results/ERR3639851_TransBorrow.gtf -n 3 -t 8$ TransBorrow -r combine_SRR10611964.gtf -b SRR10611964_genome.bam -g ref_genome.fa -s first -o ./TransBorrow_results/SRR10611964_TransBorrow.gtf -n 3 -t 8An illustration of running TransBorrow is shown as follows.Finally, the results are saved in the directory “./TransBorrow_results/” .To merge the transcripts by TACO, navigate to the TACO directory and move the three assembled GTF files generated by different assemblers to the current directory. Then, add the absolute paths of all GTF files to a TXT file as the input file for TACO. Subsequently, execute the “taco_run” command to carry out the transcript merging, which will generate a GTF file containing the merged transcripts. The recommended command using TACO is as follows.# taco_run gtf_files.txt—filter-min-expr 0.001 -o TACO_OutDirctory -p 8In this command, the “taco_run” command specifies the input TXT file. The “-p” parameter specifies the number of threads to be used, typically matching or slightly lower than the available CPU cores. The “-o” parameter is followed by the desired name for the output GTF file. The “—filter-min-expr” parameter is used to filter redundant transcripts to retain only those with expression levels above the specified threshold, and it is recommended here that a setting of 0.001 will provide more flexibility in capturing transcripts with low expression levels.$ cd TACO$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_stringtie.gtf.$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_cufflinks.gtf.$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_scallop.gtf.$ ls -R/mnt/data0/zhaody/TACO/SRR7807492_genome.* > SRR7807492_genome_merge.txt$ ls -R/mnt/data0/zhaody/TACO/ERR3639851_genome.* > ERR3639851_genome_merge.txt$ ls -R/mnt/data0/zhaody/TACO/SRR10611964_genome.* > SRR10611964_genome_merge.txt$ taco_run SRR7807492_genome_merge.txt—filter-min-expr 0.001 -o ./SRR7807492_genome_merge.gtf$ taco_run ERR3639851_genome_merge.txt—filter-min-expr 0.001 -o ./ERR3639851_genome_merge.gtf $ taco_run SRR10611964_genome_merge.txt—filter-min-expr 0.001 -o ./SRR10611964_genome_merge.gtfAn illustration of running TACO with its default sets is shown below.To merge the transcripts by StringTie-merge, navigate to the StringTie directory and move the three assemblies’ GTF file generated by different assemblers to the current directory. Subsequently, execute the “stringtie-merge” command to carry out the transcript merging, which will generate a GTF file containing the merged transcripts. The recommended command using StringTie—merge is as follows.# stringtie—merge stringtie.gtf scallop.gtf cufflinks.gtf -T 0.001 -F 0.001 -o StringTie_merge.gtfIn this command, the “—merge” parameter is followed by three input GTf files. The “-o” parameter is followed by the desired name for the output GTF file. The parameter “-T” and “-F” indicates the minimum TPM and FPKM values of transcripts to be included in the merged results, and a setting of 0.001 is recommended here to allow for more transcripts with lower expression to be merged.$ cd Stringtie$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_stringtie.gtf.$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_cufflinks.gtf.$ mv/mnt/data0/zhaody/Gtf_file/SRR7807492_genome_scallop.gtf.$ stringtie—merge SRR7807492_genome_stringtie.gtf SRR7807492_genome_cufflinks.gtf SRR7807492_genome_scallop.gtf -T 0.001 -F 0.001 -o SRR7807492_StringTie_merge.gtf$ stringtie—merge ERR3639851_genome_stringtie.gtf ERR3639851_genome_cufflinks.gtf ERR3639851_genome_scallop.gtf -T 0.001 -F 0.001 -o ERR3639851_StringTie_merge.gtf$ stringtie—merge SRR10611964_genome_stringtie.gtf SRR10611964_genome_cufflinks.gtf SRR10611964_genome_scallop.gtf -T 0.001 -F 0.001 -o SRR10611964_StringTie_merge.gtfAn illustration of running StringTie-merge with its default sets is shown below.In this protocol, we used three RNA-seq data to evaluate the performance of the assembly tools. Two of the latest mapping software, Hisat2 and Star , were emF-score, where the precision is defined as the percentage of the correctly assembled transcripts out of the candidates and recall is defined as the fraction of the correctly recovered transcripts in the ground truth, and F-score is a harmonic mean of recall and precision ) [To evaluate the performance of an assembler, we used the following metrics: the number of correctly assembled transcripts, precision, recall, and recall)) , 22.First, we compared TransBorrow to the alternatives using the data sets with different sequencing library layouts (i.e. single- or paired-end reads), and the data SRR7807492 (paired-end) and ERR3639851 (single-end) were selected as the test data. After comparison, TransBorrow exhibited superior performance in terms of the four aforementioned metrics when compared to other assemblers  and SRR10611964 (strand specific). We also compared TransBorrow to other assemblers in terms of the four aforementioned metrics. Once again, TransBorrow exhibited superior performance when compared to other assemblers (see F-score, TransBorrow also reached the highest. These results provide further evidence that TransBorrow outperformed the other compared assemblers in assembling both strand-specific and nonstranded RNA-seq data.In detail, for the correctly assembled transcripts on the nonstranded RNA-seq data SRR7807492 under Hisat2 and Star mappings, TransBorrow demonstrated significant improvement as mentioned above. For the correctly assembled transcripts on the strand-specific RNA-seq data SRR10611964 under the two aligners, TransBorrow detected 11.53% and 8.80% more expressed transcripts than StringTie, 11.08% and 10.83% more than Scallop, 68.04% and 59.81% more than Cufflinks, 22.55% and 20.06% more than StringTie-merge, and 24.78% and 19.06% more than Taco. And, for the precision and As mentioned, various RNA-seq alignment tools were available for the researchers, and Hisat2 and Star are the two most widely used mappers. In order to evaluate the performance of the assemblers under different alignment tools, we used the two different aligners to generate the mapping results of the RNA-seq data. Then we compared TransBorrow to the others using the three data sets SRR7807492, ERR3639851, and SRR10611964, under both Hisat2 and Star mappings. As compared above, TransBorrow consistently demonstrated the best performance in terms of all the aforementioned accuracy metrics regardless of the alignment tools (see Developing accurate and efficient transcript assembly software is crucial for effectively processing large-scale RNA sequencing data. It enables further exploration of the transcriptome and investigations into complex human diseases, such as cancers, which are often associated with aberrant splicing events and expression levels , 24. In TransBorrow incorporates several advantages that contribute to its superior performance. First, it effectively borrows the assembly results of other software to guide its own assembly process, which combines the strengths of multiple tools. Second, TransBorrow integrates the assemblies of other assembles to build a novel graph model, namely color graph, which facilitates efficient extraction of reliable subpaths. Third, TransBorrow constructs the weighted line graph based on the splice graph and the extracted reliable subpaths. Fourth, TransBorrow introduces a new strategy for identifying transcript-representing paths from the weighted line graphs.de novo assemblies, and the relatively complex installation process. In the future, we will definitely tackle these problems.Although we have seen great advantages taken by TransBorrow, the current version of TransBorrow does have several limitations, such as the inability to assemble long reads, incompatible with In conclusion, TransBorrow stands out among existing software by accurately assembling complex raw sequencing data into expressed transcripts. By utilizing TransBorrow, researchers can effectively harness the power of RNA-seq for advanced transcriptome analysis.


An early gastric cancer was found on the gastric body in an 85-year-old man. Subsequently, an endoscopic submucosal dissection (ESD) was performed . AlthouVideo 1 Successful clip closure for a delayed perforation after gastric endoscopic submucosal dissection.Delayed perforation after gastric ESD is an extremely rare complication and is often managed surgicallyEndoscopy_UCTN_Code_TTT_1AO_2AG


Electroacupuncture (EA) is a beneficial physiotherapy approach for addressing neuropsychiatric disorders. Nevertheless, the impact of EA on the gut microbiome in relation to anxiety disorders remains poorly understood.To address this gap, we conducted a study using a chronic restraint stress (CRS) mouse model to investigate the anti-anxiety outcome of EA and its influence on gut microbiota. Our research involved behavioral tests and comprehensive sequencing of full-length 16S rRNA microbiomes.Our findings revealed that CRS led to significant anxiety-like behaviors and an imbalance in the gut microbiota. Specifically, we identified 13 species that exhibited changes associated with anxiety-like behaviors. Furthermore, EA partially alleviated both behaviors related to anxiety and the dysbiosis induced by CRS.In summary, this study sheds light on the alterations in gut microbiota species resulting from CRS treatment and brings new light into the connection between EA’s anti-anxiety effects and the gut microbiota. They affect approximately 12.1% of the global population . These dElectroacupuncture (EA) is a modernized adaptation of traditional Chinese medicine’s acupuncture, a technique known for its significant impact on the nervous, endocrine, and immune systems . EA sharThe gut-brain axis, representing the bidirectional connection between the gut microbiota and the central nervous system (CNS), is a crucial player in regulating emotions and stress. It operates through various mechanisms, including metabolic, neural, hormonal, and immune-mediated pathways . A substIn this context, our current study aimed to investigate how EA treatment impacts the gut microbiome in a murine model of CRS. This is a well-established and widely accepted stress-related anxiety model that reliably induces anxiety-like behavior . Simulta2.2.1.Male C57BL/6 mice, aged 8 weeks and with a weight between 18 and 22 g, were supplied by the Animal Center of Air Force Military Medical University. These mice were group-housed in cages, with each cage accommodating four mice. They had unrestricted access to food and water and were kept in a controlled environment at a temperature of 20–25°C. The cages had wire bottoms, and the mice followed a 12-h light/dark cycle, with lights on from 8:00 a.m. to 8:00 p.m. The research procedures conducted in this study received approval from the Ethics Committee of Xi’an Gaoxin Hospital under the reference number 2023-GXKY-0011. All experiments were conducted in accordance with the guidelines provided in the National Institutes of Health Guide for the Care and Use of Laboratory Animals.2.2.n = 8) and CRS (n = 16). In the CRS group, mice were exposed to CRS for 2 h per day over a continuous 14-day period. This involved placing the mice in conical tubes (50 mL) equipped with airflow holes. In contrast, the Control group mice were transferred from their original cage to an experimental room, delicately managed for 5 min, and then transported back to their holding place 2 h later. This procedure was repeated over the course of 14 days  for the EA procedures. Consistent with previous protocols , mice re2.4.The open field chamber used in this study was constructed from white polycarbonate and measured 50 cm × 50 cm in size. Each mouse was positioned in the middle of the chamber, and their behavior was monitored and recorded for a duration of 5 min. We utilized an overhead video-tracking and analysis system acquired from Top Scan, Clever Sys Inc. (United States) for this purpose. Specifically, we measured the time that mice spent within the central area, which was a 25 cm × 25 cm square at the center of the chamber, as well as the whole distance they covered during the observation period.2.5.The maze apparatus used in our study, supplied by Dig Behav, Ji Liang Co. Ltd. (China) was comprised of two open arms (each measuring 35 cm × 6 cm) and two enclosed arms (each measuring 35 cm × 6 cm), elevated 50 cm over the floor. During the testing phase, mice were initially positioned in the central square of the maze, oriented toward one of the open arms. Their behavior was then observed and recorded for 5 min. We quantified the number of entries into the open arms (entry count) and the duration of placement within the open arms, employing the same monitoring system as used in the OFT. All tests occurred in low light conditions, and the test area was thoroughly sanitized with 30% ethanol after each trial.2.6.http://rdp.cme.msu.edu/. Following the OTU assignment and taxonomy analysis, subsequent analyses including Alpha Diversity Analysis, Linear Discriminant Analysis (LDA), Principal Coordinate Analysis (PCoA), and correlation analysis were performed using the Majorbio cloud platform, which is provided by Majorbio Bio-Pharm Technology Co., Ltd.Each mouse was placed in a metabolic cage between 7:00 a.m. and 11:00 a.m., and fecal samples were collected in sterile cryotubes, and immediately frozen in liquid nitrogen before further analysis. Undefecated mice promote defecation by lifting their tails, ensuring that each mouse collects at least one fecal sample. Genomic DNA was then obtained from these fecal samples using the E.Z.N.A. Stool DNA Kit, manufactured by Omega Bio-Tek, United States . For the2.7.p < 0.05 was deemed statistically significant.Statistical analyses were performed with the GraphPad v.8.0 or SPSS 21.0 software . We first assessed the normal distribution of continuous data using the Shapiro–Wilk test. If the data met the criteria for normal distribution or variance homogeneity, we conducted unpaired t-tests or one-way analysis of variance (ANOVA), and subsequent Bonferroni post-hoc tests for pairwise comparisons. Alternatively, if the data did not meet these criteria, nonparametric tests such as the Wilcoxon rank-sum test or Kruskal-Wallis test were applied. To examine correlations between behaviors and gut microbiota at the species level, we utilized Spearman’s rank correlation coefficient. All significance tests were two-tailed, and a significance level of 3.3.1.t = 0.355, df = 22, p = 0.726; t = 0.443, df = 22, p = 0.662; t = 4.535, df = 22, p < 0.001; t = 2.321, df = 22, p = 0.03; t = 2.654, df = 22, p = 0.015; p = 0.04, r2 = 0.258, p = 0.001; r2 = 0.281, p = 0.001; r2 = 0.124, p = 0.001; No significant difference was observed in the whole distance traveled in the OFT  analysis, as illustrated in Muribaculaceae, Streptococcaceae and Burkholderiaceae; genus Duncaniella, unclassified_f_Muribaculaceae, Limosilactobacillus, Mammaliicoccus, Lactococcus, Ralstonia, unclassified_f_Eggerthellaceae and Streptococcus; species Ralstonia_pickettii, Streptococcus_danieliae, unclassified_g_Lactococcus, Limosilactobacillus_reuteri, Mammaliicoccus_sciuri, Odoribacter_laneus, unclassified_f_Muribaculaceae, Duncaniella_freteri, Bacteroides_caecimuris and unclassified_f_Eggerthellaceae were enriched in the Control group. Whereas phylum Candidatus_Melainabacteria; family Bacteroidaceae, Prevotellaceae, unclassified_o_Bacteroidales, unclassified_o_Vampirovibrionales, Clostridiaceae, Eubacteriaceae, unclassified_o_Rhodospirillales and Desulfovibrionaceae; genus unclassified_f_Prevotellaceae, Eubacterium, Muribaculum, Bacteroides, Vampirovibrio, unclassified_o_Bacteroidales, Oscillibacter, Phocea, Sporobacter, Acetatifactor, Phocaeicola, Mailhella, unclassified_o_Rhodospirillales, Anaerotignum, Harryflintia and Clostridium; species Harryflintia_acetispora, Bacteroides_acidifaciens, Bacteroides_uniformis, Phocaeicola_sartorii, Muribaculum_intestinale, unclassified_f_Prevotellaceae, Alistipes_finegoldii, unclassified_o_Bacteroidales, Vampirovibrio_chlorellavorus, unclassified_g_Clostridium, Eubacterium_coprostanoligenes, Acetatifactor_muris, Anaerotruncus_colihominis, Anaerotruncus_rubiinfantis, Oscillibacter_valericigenes, unclassified_g_Oscillibacter, Phocea_massiliensis, Sporobacter_termitidis, unclassified_o_Rhodospirillales, Mailhella_massiliensis and Helicobacter_bilis were enriched in the CRS group.We found distinctions in taxonomic composition between the Control and CRS groups using both LDA with a threshold of LDA ≥ 3 and a significance level of unclassified_o_Bacteroidales, unclassified_g_Christensenella, Odoribacter_splanchnicus, Acutalibacter_muris, Bacteroides_uniformis, Oscillibacter_valericigenes and Acetatifactor_muris; but had a positive correlation with the abundance of Lactobacillus_gasseri, Limosilactobacillus_reuteri, Akkermansia_muciniphila and Ligilactobacillus_murinus. The No. of entries into open arms had a negative correlation with the abundance of Bacteroides_acidifaciens and Eubacterium_coprostanoligenes. The time spent in center had a negative correlation with the abundance of unclassified_g_Anaerotruncus, Phocaeicola_sartorii, unclassified_f_Prevotellaceae, Mailhella_massiliensis, Vampirovibrio_chlorellavorus, Bacteroides_acidifaciens, Bacteroides_uniformis and Acetatifactor_muris; whereas it had a positive correlation with the abundance of unclassified_f_Muribaculaceae, Duncaniella_freteri, Bacteroides_caecimuris and Lactobacillus_gasseri . Notably, the application of EA (CRS + EA) partially mitigated the anxiety-like behavior induced by CRS in mice, as indicated by an increase in the time the animals stayed in the center area of the OFT and the open arms of the EPMT when compared to the CRS + fEA group (p < 0.05).As depicted in 3.4.r2 = 0.131, p = 0.012; r2 = 0.112, p = 0.159; r2 = 0.108, p = 0.117; Lactobacillaceae, genus Escherichia and Olsenella, species unclassified_g_Lactobacillus, unclassified_g_Olsenella, Escherichia_fergusonii and Lactobacillus_gasseri. Meanwhile, phylum Proteobacteria; family Kiloniellaceae; genus Butyribacter and Aestuariispira; species Parabacteroides_gordonii, Aestuariispira_insulae, Parabacteroides_goldsteinii, Butyribacter_intestini, Bacteroides_uniformis, Eubacterium_coprostanoligenes and Bacteroides_acidifaciens were enriched in the CRS + fEA group. Phylum Candidatus_Melainabacteria; family Prevotellaceae and unclassified_o_Vampirovibrionales; genus Faecalimonas, Vampirovibrio and Lachnoclostridium; species Faecalimonas_umbilicata, Vampirovibrio_chlorellavorus and unclassified_g_Lachnoclostridium were enriched in CRS + EA group , separation anxiety, selective mutism, specific phobias, social anxiety disorder (SAD), panic disorder, and agoraphobia . The linive mice . Given tEA is an alternative therapeutic approach that combines traditional acupuncture techniques with electrotherapy and is increasingly recognized for its potential in treating neuropsychiatric disorders. A growing body of research has highlighted the beneficial effects of EA, particularly when applied at the “Bai hui” (GV20) acupoint. Studies have shown that GV20-based EA can regulate basic fibroblast growth factor (FGF2) in the rat hippocampus , enhanceParasutterella and Bacteroides while decreasing the relative abundances of Dialister, Hungatella, Megasphaera, Barnesiella, Allisonella, Intestinimon and Moryella at the genus level in the treatment of Parkinson’s disease  has been well-declared , This in disease . Preclin disease . HoweverOur present study delved into the composition of the gut microbiome in mice subjected to CRS modeling after EA treatment. Similar to other neuromodulation therapies , EA had Lactobacillus_gasseri was more abundant in the Sham group and it was positively correlated with the number of entries into open arms. Conversely, Bacteroides_uniformis was enriched in the CRS + fEA group and negatively correlated with the number of entries into open arms and the time the subjects remained in the center. Lactobacillus_gasseri is a probiotic (Bacteroides_uniformis has been associated with the adverse effects of a depressive microbiome on behavior (Eubacterium_coprostanoligenes is a species that has a lipolytic function (Furthermore, our analysis revealed that robiotic  whereas behavior . These ffunction . Its abu5.In summary, our findings highlight that CRS leads to pronounced anxiety-like behaviors and disturbances in gut microbiota composition, and these effects can be partially mitigated through EA treatment. We further investigated the specific microbial species associated with anxiety-like behaviors at the species level, identifying 13 species linked to the anxiety-like responses induced by CRS. However, it remains to be explored how longer-duration and varying parameters of EA may impact gut microbiota composition.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The animal study was approved by the research procedures conducted in this study received approval from the Ethics Committee of Xi’an Gaoxin Hospital under the reference number 2023-GXKY-0011. The study was conducted in accordance with the local legislation and institutional requirements.JB: Conceptualization, Investigation, Writing – original draft. J-QW: Conceptualization, Data curation, Writing – original draft. QT: Investigation, Writing – original draft. FX: Formal analysis, Funding acquisition, Writing – original draft. WZ: Methodology, Writing – original draft. HH: Conceptualization, Writing – review & editing.


The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the “guilt-by-association” principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an  Key Points:Most metabolites have little or no information available in the literature.We propose an original method leveraging information contained in the literature from metabolic neighbors.We provide more than 35000 suggested relations between overlooked metabolites and disease-related concepts.the Matthew effect  = P. Under this assumption, for any contributor i, the prior distribution of ip is modeled as a Beta distribution parameterized by mean (μ = P) and sample size (ν):The probability portions . We assuP, a relationship would not be suggested a priori, and the higher ν, the more each contributor i would have to bring new evidence (in) to change this prior belief , is much more sensitive to outlier contributors than LogOdds [LogOdds should be considered a measure of significance and Log2FC as a measure of effect size. Finally, LogOdds and Log2FC can also be computed independently for each contributor i using their associated component in the prior , only the distribution priorf is used to compute LogOdds and Log2FC. In summary, for metabolites without literature, LogOdds and Log2FC are derived from priorf, while for metabolites with literature, they are obtained from postf. For the latter, priorLogOdds and priorLog2FC are computed from the prior distribution priorf and aim to represent the belief of the metabolic neighborhood, without the influence of the compound’s literature.For metabolites mentioned in few articles and with literature available in the neighborhood (2), the behavior of the method is exactly as described above. When the compound Beta(α(0), β(0)), and then the posterior distribution is Beta(α(0), β(0)) is used, but predictions are automatically discarded.There may be no literature available in the neighborhood of some metabolites. In this case, the prior distribution is simply defined by Since the construction of the prior from the neighborhood’s literature is critical in the proposed method, several diagnostic values are also reported to judge its consistency. Those additional indicators are detailed in Project name: Forum-LiteraturePropagationhttps://github.com/eMetaboHUB/Forum-LiteraturePropagationProject homepage: Operating system(s): Platform independentProgramming language: Python, bash scriptOther requirements: Python 3.7, Pip, CondaLicense: CeCILL 2.1RRID: SCR_023874giad065_GIGA-D-23-00014_Original_SubmissionClick here for additional data file.giad065_GIGA-D-23-00014_Revision_1Click here for additional data file.giad065_GIGA-D-23-00014_Revision_2Click here for additional data file.giad065_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad065_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giad065_Reviewer_1_Report_Original_SubmissionBiswapriya Misra -- 2/25/2023 ReviewedClick here for additional data file.giad065_Reviewer_2_Report_Original_SubmissionTara Eicher, M.S. -- 3/3/2023 ReviewedClick here for additional data file.giad065_Reviewer_2_Report_Revision_1Tara Eicher, M.S. -- 6/22/2023 ReviewedClick here for additional data file.giad065_Reviewer_3_Report_Original_SubmissionBrian DeFelice -- 3/15/2023 ReviewedClick here for additional data file.giad065_Reviewer_3_Report_Revision_1Brian DeFelice -- 6/22/2023 ReviewedClick here for additional data file.giad065_Supplemental_FileClick here for additional data file.


This novel device appears to be a promising new method for weight reduction that is fast, feasible, and safe . RandomEndoscopy_UCTN_Code_TTT_1AO_2AN


To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation. Drosophila melanogaster, the collective profile of gene expressions in each cell type or tissue does not remain static, since genes are continuously regulating each other ][‘GMT’]))# GMT_base/POTFUL-Uncut.gmt# GMT_base/POTFUL-3hpc.gmtf.fig = POT.Plots[Samples[0]][‘WGCNA_BarPlot’]fig.show# fig = POT.Plots[Samples[1]][‘WGCNA_BarPlot’]fig.show# Using the following Python script, a bar chart of the numbers of the genes in each WGCNA module for each dataset was created a,b:fig Note: The “fig” is a Plotly figure object that can be further modified accordingly to export a publication quality image, as described below:fig.update_layout’,         font=dict)fig.update_xaxesfig.update_yaxesfig.write_imagefig.write_imageg.p-value was calculated (hypergeometric test), indicating whether the overlap between the two module gene lists is significant. As the background parameter, the nodes of both co-expression networks that were being compared were used. For assigning significance color codes and significance asterisks, only ‘Adjusted p-value’ is considered by default. An enrichment analysis of modules was performed of one sample concerning another sample using the following command:POT.WGCNA_Module_EnrichmentUsing Fisher’s exact test, the Note: The results of the module enrichment analysis can be accessed as a Python (Pandas) dataframe using the following command:print(POT.Data[“Enrichment_Dotplot”])h.fig = POT.Plots[“Enrichment_Dotplot”]fig.update_layout)fig.write_imagefig.write_image(POT.OutDir+f”3hpc__UncutEnri_dot.svg”)# Using the following Python command, the enrichment dot plot was generated, and a high-quality image was exported. Every dot in the enrichment dot represents the significance of the enrichment, i.e., green (***), gold (**), and yellow (*). In contrast, the plus (+) symbol represents not significantly enhanced sets.Note: Each WGCNA module of samples on the y-axis (uncut) was compared to samples on the x-axis (3hpc). The order of samples in the “WGCNA_Module_Enrichment” function was changed to do the comparison in the other direction, i.e., (uncut vs. 3hpc) ). Additionally, the “WGCNA_Module_Enrichment” function only accepts two samples.Duration: 5 mina.# UncutPOT.TF_reg# 3hpcPOT.TF_reg(Samples[1])The TF–target pairs that did not belong to the known curated TF–target pairs were filtered out using the following Python command for each sample:Note: We could choose whether to do this step or not. We included this choice to help deal with large numbers of TF–target pairs created by prediction tools like GENI3. The purpose of removing some pairs is to make the analysis smoother, especially when there are many of pairs to go through.b.# UncutPOT.merge_reg_coexp(Samples[0])# 3hpcPOT.merge_reg_coexp(Samples[1])Using the following Python command, the remaining GRN-weighted network was matched with the co-expression network to keep only those pairs that are co-expressed and involved in regulation:Note: The network of node pairs that are co-expressed and are TF–target pairs is called the co-expressed–GRNs.c.# UncutPOT.network_centrality(Samples[0])# 3hpcPOT.network_centrality(Samples[1])Network centrality analysis was performed on the co-expressed–GRN using the following command # 3hpcPOT.generate_graphml_out(Samples[1])The GraphML file was generated, and the network visualized using the following command (see e.# UncutPOT.Graph_vis(Samples[0])POT.Plots[Samples[0]][‘Network_Viz’] .show(POT.OutDir+’Uncut.html’)# # 3hpcPOT.Graph_vis(Samples[1])POT.Plots[Samples[1]][‘Network_Viz’] .show(POT.OutDir+’3hpc.html’)# The CERN was plotted and exported using the following command:f.POT.netowork_overlap# There are 20 nodes overlapping between pair of GraphsPOT.Plots[‘Overlap_Network_Viz’].show(‘Overlap.html’)# The co-expressed–GRNs of both samples were compared and plotted to check for any overlapping nodes, using the following command:


Endoscopic resection of duodenal gastrointestinal stromal tumors (GISTs) is challenging with non-negligible complicationsA 59-year-old man was referred for a 2-cm muscle-origin tumor in the duodenal bulb ; the diVideo 1 Endoscopic suturing can rescue the defect of endoscopic full-thickness resection for a duodenal gastrointestinal stromal tumor.Endoscopy_UCTN_Code_TTT_1AO_2AC


Peroral endoscopic myotomy (POEM) is an increasingly adopted strategy for the treatment of Zenker’s diverticulum . The Z-Video 1 Peroral endoscopic myotomy for Zenker’s diverticulum without tunneling.After the initial mucosal incision, both submucosal sides of the septum are lifted, with a mixture of hydroxyethyl starch and indigo carmine. Then we proceed to direct myotomy of the septum . The diEndoscopy_UCTN_Code_TTT_1AO_2AGCitation Format10.1055/a-2127-7402.Endoscopy 2023; 55: E946–E948. doi: