text: 244,162,1316458822135767040,1210312444221935616,Cyrus Rashtchian,"[1/4] Challenge: recover an unknown string, but you only see noisy copies, bits randomly deleted. How many samples do you need? New survey paper ""Trace Reconstruction Problems in Computational Biology"" w/ Vinnu Bhardwaj, Pavel A. Pevzner @yana_safonova_  <LINK> [2/4] We've been working on this for about a year. It has two main goals: 1) Describe many new models motivated by computational immunology, so that #MachineLearning / #Statistics / #compbio ppl work on them! 2) Provide a coherent survey of the lots of recent work in the area <LINK> [3/4] The above pic shows some models, where instead of indels, there is random trimming/errors in prefix or suffix. This is motivated by VDJ recombination, which is a crucial and very cool way that your body fights off bad pathogens (leading to the 1987 #NobelPrize !). <LINK> [4/4] Beyond immunology, we also discuss #DNA data storage and why the trace reconstruction problems show up naturally when you are recovering the data. We also describe the main theoretical advances, with even more open questions! <LINK>",https://arxiv.org/abs/2010.06083,"The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions. ",Trace Reconstruction Problems in Computational Biology,4,"['[1/4] Challenge: recover an unknown string, but you only see noisy copies, bits randomly deleted. How many samples do you need?\n\nNew survey paper ""Trace Reconstruction Problems in Computational Biology"" w/ Vinnu Bhardwaj, Pavel A. Pevzner @yana_safonova_ \n\n<LINK>', ""[2/4] We've been working on this for about a year. It has two main goals: \n1) Describe many new models motivated by computational immunology, so that #MachineLearning / #Statistics / #compbio ppl work on them!\n2) Provide a coherent survey of the lots of recent work in the area https://t.co/fYHDTNlH9g"", '[3/4] The above pic shows some models, where instead of indels, there is random trimming/errors in prefix or suffix. This is motivated by VDJ recombination, which is a crucial and very cool way that your body fights off bad pathogens (leading to the 1987 #NobelPrize !). https://t.co/2fprDA6WCj', '[4/4] Beyond immunology, we also discuss #DNA data storage and why the trace reconstruction problems show up naturally when you are recovering the data. We also describe the main theoretical advances, with even more open questions! https://t.co/ookRHrLB0O']",20,10,1062