X + Y, are classic and important problems. Here, a new algorithm is presented, which generates the top k values of the form Selection and sorting the Cartesian sum,  Note that this problem definition is presented w.l.o.g.; X and Y need not share the same length. Top-k is important to practical applications, such as selecting the most abundant k isotopologue peaks from a compound (k is ∈ Ω(n + k), because loading the vectors is ∈ Θ(n) and returning the minimal k values is ∈ Θ(k).Given two vectors of length compound . Top-k ik can be solved trivially in n2 values of the form Xi + Yj. By using median-of-medians (O(n2) steps by generating all n2 values and performing k-selection on them.Top--medians , this cakth minimum value from X + Y in In 1982, Frederickson & Johnson introduced a method reminiscent of median-of-medians ; their m)) steps .k smallest elements from a min-heap in O(k), assuming the heap has already been built  solves top-k in O(n + k). The tree data structure in Frederickson’s method can be combined with a combinatoric heap to compute the kth smallest value from X + Y.Frederickson subsequently published a second algorithm, which finds the en built . Combinikth smallest value . Then, as tuple  is popped from soft heap, lower-quality tuples are inserted into the soft heap. These lower-quality tuples of  areKaplan et al. described an alternative method for selecting the st value ; that mest value . By heapXi + Yj , this scheme progresses in row-major order, thereby avoiding a tuple being added multiple times.In the matrix kth smallest value from X + Y, the best k values are popped from the soft heap. Even though only the minimal k values are desired, “corruption” in the soft heap means that the soft heap will not always pop the minimal value; however, as a result, soft heaps can run faster than the t · ε, where t is the number of insertions into the soft heap thus far. Thus, instead of popping k items  values, which must include the minimal k values, are popped. These values are post-processed to retrieve the minimal k values via linear time one-dimensional selection .To compute the election . For conk in O(n + k) steps; this is because computing the kth smallest value from X + Y pops the minimal k values from the soft heap.Note that the Kaplan et al. method easily solves top-X + Y. LOHs are stricter than heaps but not as strict as sorting: Heaps guarantee only that Xi ≤ Xchildren(i), but do not guarantee any ordering between one child of Xi, a, and the child of the sibling of a. Sorting is stricter still, but sorting n values cannot be done faster than Xu)( = Xu). The size of these layers starts with |X(1)| = 1 and grows exponentially such that u children from layer u + 1, this can be seen as a more constrained form of the heap; however, unlike sorting, for any constant α > 1, LOHs can be constructed ∈ O(n) by performing iterative linear time one-dimensional selection, iteratively selecting and removing the largest layer until all layers have been partitioned. For example, 8,1,6,4,5,3,2 can be LOHified with α = 2 into an LOH with three layers  by first selecting the largest 4 values on the entire list , removing them, and then selecting the largest 2 values from the remaining 3 values .This paper uses layer-ordered heaps (LOHs)  to produα LOHs has been necessary to demonstrate that for k values from X1 + X2 + ⋯ + Xm (where each Xi has length n) in α is not trivial when X(1)| = |X(2)| = ⋯ = 1, indicating a sorting, which implies a runtime ∈ Ω(nlog(n))  . Furthernlog(n)) .python implementation of a LOH is shown in listing 1.A k presented here makes extensive use of LOHs. It is simple to implement, does not rely on anything more complicated than linear time one-dimensional selection . Due to its simplicity and contiguous memory access, it has a fast performance in practice.The new, optimal algorithm for solving top-The algorithm presented is broken into phases. An illustration of these phases is provided in X and Y. This is performed by using linear time one-dimensional selection to iteratively remove the largest remaining layer  both Xu)( and Yv)( are layers of their respective LOHs.Now layer products of the form Xu)( and Yv). The algorithm proceeds by popping the lexicographically minimum tuple from H. W.l.o.g., there is no guaranteed ordering of the form Xu)( + Yv)( ≤ Xu(+ 1) + Yv)( + Yv) > min(Xu(+ 1) + Yv); however, lexicographically, H only after ⌊⌋ has been popped from H. Note that for this reason and to break ties where layer products contain identical values,  are included in the tuple. ⌈⌉ tuples do not insert any new tuples into H when they’re popped.Binary heap u,v)⌉ is popped from H, the index  is appended to list q and the size of the layer product |Xu)( + Yv)(| = |Xu)(|·|Yv) appended to list q. s′ is the total number of elements in each of these  layer products appended to q during phase 2.Any remaining tuple in q are generated. A linear time one-dimensional k-selection is performed on these values and returned.The values from every element in each layer product in q must contain the minimal k values in X + Y. Thus, by performing one-dimensional k-selection on those values in phase 3, the minimal k values in X + Y are found.Lemma 2.4 proves that at termination all layer products found in Lemma 2.1.If  > 1) and  1) must previously have been popped from H.Proof. There is a chain of pops and insertions backwards from u, v = 1, the lemma is true.When both u = 1 this chain is of the form W.l.o.g. if H increment either row or column, something of the form H before inserting a = u, then a < u, then from the insertion of H, until H must contain something of the form H. Because there are a finite number of these a' and they are not revisited, before Otherwise, both Lemma 2.2If .Proof. Inserting  1) and  1). These pops will insert will therefore be popped before Lemma 2.3All tuples will be visited in ascending order as they are popped from H.Proof. Let H and let a < u, b ≤ v, or w.l.o.g. a < u, b > v. In the former case, H until In the latter case, lemma 2.1 says that H, H, then H. Each Ordering on popping with H until Identical reasoning also shows that □Thus, all tuples are popped in ascending order. Lemma 2.4At the end of phase 2, the layer products whose indices are found in q contain the minimal k values.Proof. Let  be the layer product that first makes s≥ k. There are at least k values of X + Y that are q at the end of phase 1 can only be improved by trading some value for a smaller value, and thus require a new value <max(Xu)( + Yv).H in ascending order; therefore, any layer product q and cannot improve it. Thus the only layers that need be considered further have had H but not yet popped.By lemma 2.3, tuples will be popped from q all such remaining layer products of interest. Thus, at the end of phase 2, q contains all layer products that will be represented in the k-selection of X + Y. □Phase 2 appends to python implementation of this method is shown in listing 2.A O(n + k).Theorem 2.8 proves that the total runtime is ∈ Lemma 2.5Let  be a layer product appended to q during phase 2. Either u′ = 1, v′ = 1, or  was already appended to q in phase 1.Proof. Let u′>1 and v′>1. By lemma 2.3, minimum and maximum layer products are popped in ascending order. By the layer ordering property of X and Y, □Lemma 2.6s, the number of elements in all layer products appended to q in phase 1, is ∈ O(k).Proof.  is the layer product whose inclusion during phase 1 in q achieves s ≥ k; therefore, . This happens when H.If k = 1, popping .k > 1, then at least one layer index is >1: u > 1 or v > 1. W.l.o.g., let u > 1. By lemma 2.1, popping H requires previously popping Xu)( + Yv)(|∈ O(|Xu(−1) + Yv)(|). |Xu(−1) + Yv)( + Yv)( + Yv)(|∈ O(k). s ∈ O(k). □If Lemma 2.7s′, the total number of elements in all layer products appended to q in phase 2, ∈ O(k).Proof. Each layer product appended to q in phase 2 has had u′ = 1 or v′ = 1 or u′ > 1 and v′ > 1. Each  matches exactly one layer product . Because s, the count of all elements whose layer products were inserted into q in phase 1, includes Xu′)( + Yv′)( (the latter is appended to q during phase 2). By exponential growth of layers in X and Y, Xu′(−1) + Yv′(−1)| values were included in s during phase 1, and thus the total number of elements in all such  layer products is ≤ s. Thus the sum of sizes of all layer products  with u′ > 1 and v′ > 1 that are appended to q during phase 2 is asymptotically ≤ α2· s.First consider when u′ = 1 or v′ = 1, the number of elements in all layer products must be ∈ O(n): u′ = 1 or v′ = 1 are ∈ O(k):When either u′>1, H only when H at any time. Furthermore, popping H requires previously popping H: layer ordering on X implies max(Xu′(−1)) ≤ min (Xu′) and |Y(1)| = 1 implies min(Y(1)) = max(Y(1)), and so H and counted in s. By the exponential growth of layers, the contribution of all such u′ > 1, v′ = 1 will be u′ > 1, v′ = 1 or u′ = 1, v′ > 1 will be ≈ ≤ 2 α · s.W.l.o.g. for u′ = v′ = 1, the layer product contains 1 element.When s′, the total number of elements found in layer products appended to q during phase 2, has s′∈ O(k). □Therefore, Theorem 2.8The total runtime of the algorithm is ∈ O(n + k).Proof. For any constant α > 1, LOHification of X and Y runs in linear time, and so phase 0 runs ∈ O(n).α(n); therefore, the total number of layer products is 2α(n) elements, because each layer product may be inserted as both orO(n) runtime of phase 0.The total number of layers in each LOH is ≈ logs∈ O(k). Likewise, lemma 2.7 shows that s′∈ O(k). The number of elements in all layer products in q during phase 3 is s + s′∈ O(k). Thus, the number of elements on which the one-dimensional selection is performed will be k-selection in phase 3 is ∈ O(k).Lemma 2.6 shows that O(n + k + k + k) = O(n + k). □The total runtime of all phases ∈ O(n2log(n) + k) method (chosen for reference because it is the easiest method to implement and because of the fast runtime constant on python’s built-in sorting routine), the soft heap-based method from Kaplan et al., and the LOH-based method in this paper are shown in Runtimes of the naive kth best value Xi + Yj occurs. It is somewhat reminiscent of skip lists ). But unlike soft heaps, LOHs can be constructed easily using only an implementation of median-of-medians .The algorithm can be thought of as “zooming out” as it pans through the layer products, thereby passing the value threshold at which the ip lists ; howeverk appears in the runtime formula. This is significant because the layer products in q at the end of phase 2 could be returned in their compressed form . The total runtime of phases 0–2 is ∈ O(n). It may be possible to recursively perform X + Y selection on layer products Xu)( + Yv).Phase 3 is the only part of the algorithm in which X and Y are extended on the fly or where several subsequent selections are performed), whereas listing 2 could be adapted to those uses.As noted in theorem 2.8, even fully sorting all of the minimum and maximum layer products would be Phase 0 (which performs LOHification) is the slowest part of the presented python implementation; it would benefit from having a practically faster implementation to perform LOHify.The fast practical performance is partially due to the algorithm’s simplicity and partially due to the contiguous nature of LOHs. Online data structures like soft heap are less easily suited to contiguous access, because they support efficient removal and therefore move pointers to memory rather than moving the contents of the memory.α affects performance through the cost of LOHifying and the amount by which the number of generated values overshoots the k minimum values wanted: when α ≈ 1, LOHify effectively sorts X and Y, but generates few extra values; k-selection.The choice of k values from X + Y. The new optimal algorithm presented here is faster in practice than the existing soft heap-based optimal algorithm.LOHs can be constructed in linear time and used to produce a theoretically optimal algorithm for selecting the minimal Listing 1.LayerOrderedHeap.py: A class for LOHifying, retrieving layers, and the minimum and maximum value in a layer.# https://stackoverflow.com/questions/10806303/python-implementation-of-median-of-medians-algorithmdef median_of_medians_select: # returns j-th smallest value:if len(L) < 10:L.sortreturn L[j]S = lIndex = 0while lIndex+5 < len(L)-1:S.append(L[lIndex:lIndex+5])lIndex += 5S.append(L[lIndex:])Meds = for subList in S:Meds.append-1)/2)))med = median_of_medians_select-1)/2))L1 = L2 = L3 = for i in L:if i < med:L1.append(i)elif i > med:L3.append(i)else:L2.append(i)if j < len(L1):return median_of_medians_selectelif j < len(L2) + len(L1):return L2[0]else:return median_of_medians_select-len(L2))def partition:n = len(array)right_n = n - left_n# median_of_medians_select argument is index, not size:max_value_in_left = median_of_medians_selectleft = right = for i in range(n):if array[i] < max_value_in_left:left.append(array[i])elif array[i] > max_value_in_left:right.append(array[i])num_at_threshold_in_left = left_n - len(left)left.extendnum_at_threshold_in_right = right_n - len(right)right.extendreturn left, rightdef layer_order_heapify_alpha_eq_2(array):n = len(array)if n == 0:return if n == 1:return arraynew_layer_size = 1layer_sizes = remaining_n = nwhile remaining_n > 0:if remaining_n >= new_layer_size:layer_sizes.append(new_layer_size)else:layer_sizes.append(remaining_n)remaining_n -= new_layer_sizenew_layer_size *= 2result = for i,ls in enumerate(layer_sizes[::-1]):small_vals,large_vals = partition - ls)array = small_valsresult.appendreturn result[::-1]class LayerOrderedHeap:def __init__:self._layers = layer_order_heapify_alpha_eq_2(array)self._min_in_layers = [ min(layer) for layer in self._layers ]self._max_in_layers = [ max(layer) for layer in self._layers ]#self._verifydef __len__(self):return len(self._layers)def _verify(self):for i in range(len(self)-1):assert(self.max(i) <= self.min(i+1))def __getitem__:return self._layers[layer_num]def min:assert( layer_num < len(self) )return self._min_in_layers[layer_num]def max:assert( layer_num < len(self) )return self._max_in_layers[layer_num]def __str__(self):return str(self._layers)Listing 2.CartesianSumSelection.py: A class for efficiently performing selection on X + Y in Θ(n + k) steps.from LayerOrderedHeap import *import heapqclass CartesianSumSelection:def _min_tuple:# True for min corner, False for max cornerreturn (self._loh_a.min(i) + self._loh_b.min(j), , False)def _max_tuple:# True for min corner, False for max cornerreturn (self._loh_a.max(i) + self._loh_b.max(j), , True)def _in_bounds:return i < len(self._loh_a) and j < len(self._loh_b)def _insert_min_if_in_bounds:if not self._in_bounds:returnif  not in self._hull_set:heapq.heappush)self._hull_set.add )def _insert_max_if_in_bounds:if not self._in_bounds:returnif  not in self._hull_set:heapq.heappush)self._hull_set.add )def __init__:self._loh_a = LayerOrderedHeap(array_a)self._loh_b = LayerOrderedHeap(array_b)self._hull_heap = [ self._min_tuple ]# False for min:self._hull_set = {  }self._num_elements_popped = 0self._layer_products_considered = self._full_cartesian_product_size = len(array_a) * len(array_b)def _pop_next_layer_product(self):result = heapq.heappop(self._hull_heap)val, , is_max = resultself._hull_set.remove )if not is_max:# when min corner is popped, push their own max and neighboring minsself._insert_min_if_in_boundsself._insert_min_if_in_boundsself._insert_max_if_in_boundselse:# when max corner is popped, do not pushself._num_elements_popped += len(self._loh_a[i]) * len(self._loh_b[j])self._layer_products_considered.append )return resultdef select:assert( k <= self._full_cartesian_product_size )while self._num_elements_popped < k:self._pop_next_layer_product# also consider all layer products still in hullfor val, , is_max in self._hull_heap:if is_max:self._num_elements_popped += len(self._loh_a[i]) * len(self._loh_b[j])self._layer_products_considered.append )# generate: values in layer products# Note: this is not always necessary, and could lead to a potentially large speedup.candidates =  for val_b in self._loh_b[j] ]print / k) )k_small_vals, large_vals = partitionreturn k_small_valsListing 3.SimplifiedCartesianSumSelection.py: A simplified implementation of Listing 2. This implementation is slower when k ≪ n2; however, it has the same asymptotic runtime for any n and k: Θ(n + k).from LayerOrderedHeap import *class SimplifiedCartesianSumSelection:def _min_tuple:# True for min corner, False for max cornerreturn (self._loh_a.min(i) + self._loh_b.min(j), , False)def _max_tuple:# True for min corner, False for max cornerreturn (self._loh_a.max(i) + self._loh_b.max(j), , True)def __init__:self._loh_a = LayerOrderedHeap(array_a)self._loh_b = LayerOrderedHeap(array_b)self._full_cartesian_product_size = len(array_a) * len(array_b)self._sorted_corners = sorted for i in range(len(self._loh_a)) for j in range(len(self._loh_b))] + [self._max_tuple for i in range(len(self._loh_a)) for j in range(len(self._loh_b))])def select:assert( k <= self._full_cartesian_product_size )candidates = index_in_sorted = 0num_elements_with_max_corner_popped = 0while num_elements_with_max_corner_popped < k:val, , is_max = self._sorted_corners[index_in_sorted]new_candidates = [ v_a+v_b for v_a in self._loh_a[i] for v_b in self._loh_b[j] ]if is_max:num_elements_with_max_corner_popped += len(new_candidates)else:# Min corners will be popped before corresponding max corner;# this gets a superset of what is needed (just as in phase 2)candidates.extend(new_candidates)index_in_sorted += 1print / k) )k_small_vals, large_vals = partitionreturn k_small_vals


With the development of distributed generation and the corresponding importance of the P.V. (photovoltaic) system, it is desired to operate a P.V. system efficiently and reliably. To ensure such an operation, a monitoring system is required to diagnose the health of the system. This paper aims to analyze a P.V. system under various operating conditions to identify parameters–derived from the I-V (current-voltage) characteristics of the P.V. system–that could serve as electrical signatures to various faulty operations and facilitate in devising a monitoring algorithm for the system. A model-based approach has been adopted to represent a P.V. system, using a one-diode model of a practical P.V. cell, developed in MATLAB/Simulink. The modelled system comprises two arrays, while each array has two panels in series. It was simulated for various operating conditions: healthy condition represented by STC (Standard Testing Condition), O.C. (open-circuited), soiling, P.S. , H.S. (panels hotspots) and P.D. (panels degradation) conditions. For the analysis of I-V curves under these conditions, six derived parameters were selected: Vte , MCPF (maximum current point factor), Ri (currents ratio), S (slope), and Dv and Di . Using these parameters, data of the actual system under various conditions were compared with its model-generated data for healthy operating conditions. Thresholds were set for each parameter’s value to mark normal operation range. It was observed that almost each considered fault creates a unique combination of sensitive parameters whose values exceeds the pre-defined thresholds, creating an electrical signature that will appear only when the corresponding conditions on the system are achieved. Based on these signatures, an algorithm has been proposed in this study which aims to identify and classify the considered faults. In comparison to other such studies, this work has been focused on those sensitive parameters for faults identification which shows greater sensitivity and contribute more to creation of unique sets of sensitive parameters for considered faults. Energy—being the necessity for economic development—has played an essential role in making up current-day civilization. The living standards of a country can be judged from energy consumption per person living there . Owing tNevertheless, like any other process, a P.V. system operation is vulnerable to certain limitations and faults . These lThe methodology opted in this work, to monitor a P.V. system against various contingent situations, belongs to electrical methods. It relies on the analysis of the I-V characteristics of the P.V. plant, using a model-based approach. By comparing these characteristics of an actual system with its modelled system with help of derived parameters from it, system’s health could be inferenced. Such an approach has been opted in this work using One-diode model with improved parameters for modelling a P.V. system, as it is uncomplicated and accurate enough to represent a practical P.V. plant operation . The modsh) and series (Rs) resistances account for the losses in a practical P.V. cell operation. The equation that describes the One-diode model is given by Eq  is charge on an electron, K.B. (1.38065×10−23 J/K) is Boltzmann constant and A is the diode ideality factor of a P.V. cell. For several cells connected in series (Ns), to make a panel/module, the equation becomes,While observing Eq , Io is tWhere asIo, is given by Eq  and short-circuit current (Iscn) at STC. Eq , and Tn (in Kelvin) represents the temperature of the panel at STC. These equations can be solved using data from the P.V. panels manufacturer datasheet along with irradiance and temperature values for any operating condition. Literature study reveals that a pyranometer at the panel’s surface can estimate the irradiance very well, while temperature of the panels can be estimated using the empirical formula in Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: I Don't KnowReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: 1. There are places in the article that do not indicate which equation to cite, such as the paragraph above Equation 7.2. What does P.S. (3.1) mean in the descriptive paragraph of Figure 6?3. In the analysis of Figure 6, it is mentioned that four different regions can be set for values beyond the predefined thresholds, but it is not clearly described what the four regions are and how to define the boundary between the four regions.4. Appropriately add some relevant descriptions on how to determine the threshold range.5. Add some comparisons with other scholars' work on electrical methods.6. Further elaborate on the specific advantages or innovations of this method.Reviewer #2: 1. The introduction of this submission should be improved further. The key differences between your work and previous studies should be clarified. A point-to-point way is recommended to state the main contributions.2. PV modeling section is very traditional, and this reviewer suggests this section can be shorten properly.3. The writing style of this work likes a technical report, but not a scientific paper.4. A comparison of your method and other work should be carried out.5. Most of the figures have poor quality, such as, lacking of unit.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  26 Sep 2021Editor’s comments,Comments to the AuthorThank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We look forward to receiving your revised manuscript.1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdfAuthor’s response: The corresponding author, on behalf of all the co-authors, would like to thank the editor for the positive feedback and encouragement. The revised version of the manuscript has been updated according to the guidelines provided by the PLOS ONE style requirements. ________________________________________2. Thank you for stating the following financial disclosure: Author’s response: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.________________________________________a) Please clarify the sources of funding  for your study. List the grants or organizations that supported your study, including funding received from your institution. Author’s response: No funding or any financial grant has been received for conducting this study. ________________________________________b) State what role the funders took in the study. If the funders had no role in your study, please state:Author’s response: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript________________________________________c) If any authors received a salary from any of your funders, please state which authors and which funders.Author’s response: The authors received no specific funding for this work. ________________________________________http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. 3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see In your revised cover letter, please address the following prompts:a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail  and who has imposed them . Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.Author’s response: There are no ethical restrictions on sharing a de-identified data set. ________________________________________http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see Author’s response: The data set is uploaded in the review as a Supporting Information file. ________________________________________4. Please amend your list of authors on the manuscript to ensure that each author is linked to an affiliation.Author’s response: The list of authors is provided with affiliation in the given order, which is requested to be considered in the revised version of the manuscript. i. Muhammad Adnan Khan1,  Affiliation: Research Associate, Center for Advanced Studies in Energy University of Engineering and Technology, Phase 5, opposite to Sui Northern Gas Pipeline office, Postal address 25000, Peshawar. ii. Khalid Khan2* Affiliation: Research Associate, Center for Advanced Studies in Energy University of Engineering and Technology, Phase 5, opposite to Sui Northern Gas Pipeline office, Postal address 25000, Peshawar.iii. Adnan Daud Khan3*Affiliation: Dean Faculty Renewable Energy, Center for Advanced Studies in Energy University of Engineering and Technology, Phase 5, opposite to Sui Northern Gas Pipeline office, Postal address 25000, Peshawar.iv. Zubair Ahmad Khan4Affiliation: Professor at Department of Mechatronics, University of Engineering and Technology, Phase 5, opposite to Sui Northern Gas Pipeline office, Postal address 25000, Peshawar.v. Shahbaz Khan5Affiliation: Lab Engineer at Department of Mechatronics, University of Engineering and Technology, Phase 5, opposite to Sui Northern Gas Pipeline office, Postal address 25000, Peshawar.vi. Muhammad Rizwan Siddiqui6Lecturer at Capital University of Science and technology (CUST), Islamabad Expressway, Kahuta، Road Zone-V Sihala, Islamabad, Islamabad Capital Territory.________________________________________5. PLOS authors have the option to publish the peer review history of their article. If published, this will include your full peer review and any attached files.Author’s response: The authors agree to publish the review history of the article.________________________________________Please carefully address the comments of two reviewers to improve your paper.Editor’s comments,Concern #1:Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.Reviewer #1: PartlyAuthor response: The authors appreciate the positive remarks of the reviewer #1 and would like to reflect on any particular section of the manuscript to improve its quality, if highlighted by the reviewer. Reviewer #2: YesAuthor response: The authors appreciate the positive response of the reviewer #2.________________________________________Concern #2:Has the statistical analysis been performed appropriately and rigorously?Reviewer #1: I Don't KnowAuthor response: The authors would be happy to elaborate further on the techniques used in the article, provided specific questions are asked to be addressed. Reviewer #2: YesAuthor response: The authors appreciate the feedback of the reviewer #2.________________________________________Concern #3:Have the authors made all data underlying the findings in their manuscript fully available?The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g., participant privacy or use of data from a third party—those must be specified.Reviewer #1: YesAuthor response: The authors appreciate the positive feedback of the reviewer #1.Reviewer #2: YesAuthor response: The authors appreciate the positive feedback of the reviewer #2.________________________________________Concern #4:Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesAuthor response: The authors appreciate the positive feedback of the reviewer #1.Reviewer #2: YesAuthor response: The authors appreciate the positive response of the reviewer #2.________________________________________5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: 1. There are places in the article that do not indicate which equation to cite, such as the paragraph above Equation 7.Author’s response: The author appreciates reviewer #1 comment on this issue, as it was mistakenly left by author in the earlier draft. This mistake has been rectified and all equations are numbered properly as well as cited properly in the new draft. Equations after correction are highlighted yellow in the new draft.________________________________________2. What does P.S. (3.1) mean in the descriptive paragraph of Figure 6?Author’s response: This question has been answered more properly and the issue has been addressed in the new draft in the last paragraph of methodology section, being highlighted in yellow there. Hope this will answer reviewer’s question.________________________________________3. In the analysis of Figure 6, it is mentioned that four different regions can be set for values beyond the predefined thresholds, but it is not clearly described what the four regions are and how to define the boundary between the four regions.Author’s response: To answer this question, a brief description has been added to the new draft just below fig. (6), in highlighted text. This description has elaborated, in a brief way, on threshold levels and how they were selected. Furthermore, all the four regions regarding “Vte” are described and the boundaries between them are clearly defined in that. However, the focus of this paper is on checking the sensitivity of parameters that could serve in creating electrical signatures. Hence, it was found that Vte does the job by responding to various operating conditions in ways different enough from each other to differentiate between faults, which description tells clearly.________________________________________4. Appropriately add some relevant descriptions on how to determine the threshold range.Author’s response: This question has already been answered in question 3. Apart from that, threshold levels are defined more clearly in first paragraph of Methodology section, text highlighted in yellow. Hope that will answer the question.________________________________________5. Add some comparisons with other scholars' work on electrical methods.Author’s response: A comparison with other scholars’ work has been made at the end of results and discussions section. Highlighted in yellow.________________________________________6. Further elaborate on the specific advantages or innovations of this method.Author’s response: It has been answered in the same description where comparison with other scholars’ work has been made, as stated above.________________________________________Reviewer #2: 1. The introduction of this submission should be improved further. The key differences between your work and previous studies should be clarified. A point-to-point way is recommended to state the main contributions.Author’s response: The paper has been reviewed by the author after reviewers’ comments and the content has been improved. Hope it will satisfy the reviewer.________________________________________2. PV modelling section is very traditional, and this reviewer suggests this section can be shortened properly.Author’s response: Author appreciate reviewer’s comment on PV modelling. In response, it is stated that PV modelling contains equations for modelling the related text to describe the terms used in model equations. No extra content has been added and author has aimed to contain modelling section in as small portion as possible.________________________________________3. The writing style of this work likes a technical report, but not a scientific paper.Author’s response: Paper has been revised with minor changes as per reviewers’ comments. Hopefully the new draft resembles more like paper.________________________________________ 4. A comparison of your method and other work should be carried out.Author’s response: Done at the end of Results and Discussion chapter.________________________________________ 5. Most of the figures have poor quality, such as, lacking unit.Author’s response: This issue has been addressed in the new draft, where care has been taken while generating new figures for the paper.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 17 Nov 2021A model-based approach for detecting and identifying faults on the D.C. side of a P.V. system using electrical signatures from I-V characteristicsPONE-D-21-20995R1Dear Dr. Khan,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Lei Chen, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author  1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: All comments have been addressedReviewer #2: All comments have been addressed**********  2. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  3. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  5. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  6. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: (No Response)Reviewer #2: (No Response)**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No 23 Dec 2021PONE-D-21-20995R1 A model-based approach for detecting and identifying faults on the D.C. side of a P.V. system using electrical signatures from I-V characteristics Dear Dr. Khan:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofProfessor Lei Chen Academic EditorPLOS ONE


Mycobacterium smegmatis mc2 155 Siphoviridae temperate phages with 52,935 and 41,876 base pairs in genome length, respectively. HarryOW belongs to the A1 subcluster and Peeb to the G1 subcluster. They were isolated and annotated by students from the SUNY Old Westbury Science and Technology Entry Program.HarryOW and Peeb are  Mycobacterium smegmatis mc2 155 Siphoviridae temperate phages with 52,935 and 41,876 base pairs in genome length, respectively. HarryOW belongs to the A1 subcluster and Peeb to the G1 subcluster. They were isolated and annotated by students from the SUNY Old Westbury Science and Technology Entry Program.HarryOW and Peeb are  Mycobacterium smegmatis mc2 155 following two cycles of purification and amplification in 7H9 top agar at 37°C . Isolation was done by enrichment with  at 37°C  1). DNA. DNAMyco at 37°C . The rawhttp://cobamide2.bio.pitt.edu/computer.htm), and gene prediction tools GLIMMER v3.0  with whom it shares 94.32% gene content and 58 gene phams. The integration cassette genes of Peeb are SEA_PEEB_32, a tyrosine integrase; SEA_PEEB_33, an immunity repressor; and SEA_PEEB_34, an excise. SEA_PEEB_32 and SEA_PEEB_33 are the only two genes in the Peeb genome coded on the reverse strand. SEA_PEEB_54 is a mycobacteriophage mobile element 1 (MPME1) found in other members of this gene pham and of this cluster , a GC content of 66.6%, 62 genes, and an 11-bp 3′ sticky overhang terminus. A total of 23 of the 62 genes are of known function. Peeb is 99% genetically identical to Schiebel (GenBank accession no.  cluster .NC_052461), sharing 80.02% gene content similarity and 74 phams. As in many of the A cluster phages, the immunity repressor SEA_HARRYOW_76 is not located adjacent to the serine integrase SEA_HARRYOW_37, and the DNA primase has two overlapping reading frames, namely, SEA_HARRYOW_54 and SEA_HARRYOW_55. One of the three minor tail protein genes, SEA_HARRYOW_6, is located in the left arm of the genome, upstream of the lysin A SEA_HARRYOW_10, lysin B SEA_HARRYOW_11, and the terminase SEA_HARRYOW_12. The other two minor tail protein genes, namely, SEA_HARRYOW_26 and SEA_HARRYOW_28, are located after the tape measure gene SEA_HARRYOW_25.HarryOW is an A1 cluster mycobacteriophage with 52,935 bp, a GC content of 63.8%, 94 genes, and an 11-bp 3′ sticky overhang terminus. A total of 35 of the 94 genes are of known function. Forty genes are on the forward strand, mostly on the left arm of the genome, except for genes SEA_HARRYOW_90, SEA_HARRYOW_91, and SEA_HARRYOW_93. HarryOW is 98% genetically identical to Rutherferd (GenBank accession no. The genome sequences for Peeb and HarryOW have been deposited in GenBank and the Sequence Read Archive see .


Ovis aries) is an important agricultural species raised for meat, wool, and milk across the world. A high-quality reference genome for this species enhances the ability to discover genetic mechanisms influencing biological traits. Furthermore, a high-quality reference genome allows for precise functional annotation of gene regulatory elements. The rapid advances in genome assembly algorithms and emergence of sequencing technologies with increasingly long reads provide the opportunity for an improved de novo assembly of the sheep reference genome.The domestic sheep (Short-read Illumina (55× coverage), long-read Pacific Biosciences (75× coverage), and Hi-C data from this ewe retrieved from public databases were combined with an additional 50× coverage of Oxford Nanopore data and assembled with canu v1.9. The assembled contigs were scaffolded using Hi-C data with Salsa v2.2, gaps filled with PBsuitev15.8.24, and polished with Nanopolish v0.12.5. After duplicate contig removal with PurgeDups v1.0.1, chromosomes were oriented and polished with 2 rounds of a pipeline that consisted of freebayes v1.3.1 to call variants, Merfin to validate them, and BCFtools to generate the consensus fasta. The ARS-UI_Ramb_v2.0 assembly is 2.63 Gb in length and has improved continuity (contig NG50 of 43.18 Mb), with a 19- and 38-fold decrease in the number of scaffolds compared with Oar_rambouillet_v1.0 and Oar_v4.0. ARS-UI_Ramb_v2.0 has greater per-base accuracy and fewer insertions and deletions identified from mapped RNA sequence than previous assemblies.The ARS-UI_Ramb_v2.0 assembly is a substantial improvement in contiguity that will optimize the functional annotation of the sheep genome and facilitate improved mapping accuracy of genetic variant and expression data for traits in sheep. Ovis aries) is a globally important livestock species raised for a variety of purposes including meat, wool, and milk. Domestication likely occurred in multiple events ∼11,000 years ago [The domestic sheep , is based on a combination of Pacific Biosciences RSII WGS long-read and Illumina short-read sequencing. It has an improved contig NG50 of 2.9 megabases (Mb) and is generally regarded as the official reference assembly for global sheep research.Genome research in sheep holds promise to improve efficiency and sustainability of production and reduce the environmental effects of animal agriculture . The firde novo assembly of the same Rambouillet ewe used for Oar_rambouillet_v1.0, based on ∼50× coverage of nanopore reads (N50 47 kb) and 75× coverage Pacific Biosciences (PacBio) reads (N50 13 kb). The new assembly, ARS-UI_Ramb_v2.0, offers a 15-fold improvement in contiguity and increased accuracy, providing a basis for regulatory element annotation in the FAANG project and facilitating the discovery of biological mechanisms that influence traits important in global sheep research and production.The continued maturation of long-read sequencing technologies provided an opportunity to improve upon the sheep reference genome assembly. Because most of the proposed FAANG annotation assays had already been performed on the Rambouillet ewe, lung tissue from the same animal was chosen for DNA extraction. This allowed the use of existing long-read data to supplement new, longer-read, Oxford Nanopore PromethION sequencing. We report a The full-blood Rambouillet ewe used for this genome assembly  . The final aqueous phase was transferred to a 50-mL conical tube and the DNA precipitated with 2 mL of 5 M ammonium acetate and 15 mL of ice-cold 100% ethanol. The DNA was pulled from the alcohol using a Pasteur pipet “hook” and placed in 10 mL of cold 70% ethanol to wash the pellet. The ethanol was poured off and the DNA pellet dried for 20–30 minutes, then dissolved in a dark drawer at room temperature for 48 hours in 1 mL of 10 mM Tris-Cl pH 8.5. Library preparation for Oxford Nanopore long-read sequencing was performed with an LSK-109 template preparation kit as recommended by the manufacturer  with modifications as described by Logsdon [DNA was extracted from ∼50 mg of lung tissue using phenol:chloroform-based method as described . Briefly Logsdon . The lig Logsdon . Fastq fSequence data used in the previous Oar_rambouillet_v1.0 assembly were retrieved from the SRA listed under project number PRJNA414087 . PacBio RRID:SCR_015880) through the trimmed reads stage of assembly. Parameters for contig construction were set as “batOptions = -dg 4 -db 4 -mo 1000” [Contigs were assembled with Oxford Nanopore and PacBio reads generated as described above using canu v1.8  [Two Hi-C datasets from liver tissue from 2 different library preparations were retrieved as described above. The Hi-C reads were first aligned to the polished contigs using the Arima Genomics mapping pipeline . This pi_006646) . Salsa v_006646) .The Hi-C reads were aligned to the scaffolded assembly with the Arima Genomics mapping pipeline and then processed with PretextMap to visually evaluate the scaffolds as a contact map in PretextView . The scaRRID:SCR_016157) [RRID:SCR_012954) [RRID:SCR_010761) [RRID:SCR_005227) [k-mer consequences of variant calls and filters unsupported variants.Gap filling was completed with pbsuite v15.8.24 using both the PacBio and Oxford Nanopore reads. Nanopolish v0.12.5   with the_016157)  was used_016157) . The chr_012954) . The mit_010761)  variant _005227) , which ePRJEB35292, specifically under SRA run numbers ERR3665717 (skin), ERR3728435 , ERR3650379 (pituitary), ERR3665711 (lymph node mesenteric), and ERR3650373 (abomasum pylorus). Reads were trimmed with Trim Galore v0.6.4 [RNA sequencing data were generated from 5 tissues including skin, thalamus, pituitary, lymph node (mesenteric), and abomasum pylorus collected from the animal used to assemble the reference genome. Details regarding the RNA isolation protocol, library preparation, and sequencing as well as the raw data can be found in GenBank under BioProject e v0.6.4  and alige v0.6.4 . Indels e v0.6.4 .The annotation for ARS-UI_Ramb_v2.0, NCBI Ovis aries Annotation Release 104, is available in RefSeq and other NCBI genome resources .Here we also provide a liftover of the annotation for Oar_rambouillet_v1.0 onto ARS-UI_Ramb_v2.0. The annotation used for the liftover was NCBI v103 GCF_002742125.1_Oar_rambouillet_v1.0_genomic.fna.gz. The GFF3 format gene annotation file was prepared for processing using liftOff v1.5.2 [RRID:SCR_016582) [PRJNA414087 and PRJEB35292) to estimate transcript-level expression for every tissue as transcript per million mapped reads (TPM) and compared across the 3 annotations.To compare the breakdown of transcripts captured by the 3 annotations , we generated transcript expression estimates using Kallisto v0.44.0  . For theGCF_016772045.1, and statistics of contigs and scaffolds following initial polishing, scaffolding with Hi-C data, and manual editing, gap-filling, and final polishing, are shown in Table The 4 flow cells of PromethION data produced 136 Gb of WGS sequence (∼51× coverage) in reads having a read N50 of 47 kb. The initial generation of contigs used this data as well as 198.1 Gb of RSII data with a read N50 of 12.9 kb. The ARS-UI_Ramb_v2.0 assembly was submitted to NCBI GenBank under accession number The Themis-ASM pipeline  was implk-mer–based quality value and error rates improved with ARS-UI_Ramb_v2.0 compared with Oar_rambouillet_v1.0 and Oar_v4.0. This is also reflected in the proportion of complete assembly based on k-mers (merCompleteness), which is similar between ARS-UI_Ramb_v2.0 and Oar_rambouillet_v1.0 and both are higher than Oar_v4.0. Furthermore, the single-nucletide polymorphism (SNP) and indel quality value (baseQV) were greatest overall in ARS-UI_Ramb_v2.0 (41.84), followed by Oar_rambouillet_v1.0 (40.69) and Oar_v4.0 (32.40). The percentage of short reads not mapped to the genome was ≤1% in all 3 assemblies.The RRID:SCR_015008) v5.2.2 scores with the cetartiodactyla_odb10 dataset and metaeuk gene predictor [The completeness of ARS-UI_Ramb_v2.0 was evaluated by examining the presence or absence of evolutionarily conserved genes in each assembly using BUSCO  in the ARS-UI_Ramb_v2.0 assembly were characterized and compared with Oar_rambouillet_v1.0 by mapping 150 bp paired-end RNA-seq data from skin, thalamus, pituitary, lymph node (mesenteric), and abomasum pylorus generated from the same animal used to assemble the reference genome Table . In all The ARS-UI_Ramb_v2.0 annotation represents a substantial improvement over the annotation on Oar_rambouillet_v1.0. For example, for ARS-UI_Ramb_v2.0 16,500 coding genes have an ortholog to human , and the BUSCO scores demonstrate that 99.1% of the gene models (cetartiodactyla_odb10) are complete in the new annotation versus 98.8% in the previous one. The annotation for ARS-UI_Ramb_v2.0 includes Iso-Sequencing for 8 tissues to improve contiguity of gene models, and CAGE sequencing for 56 tissues to define transcription start sites, that were not used to annotate Oar_rambouillet_v1.0.Using Kallisto we compared the number of expressed transcripts, for the RNA-Seq dataset of 61 tissue samples from Benz2616, across the 3 annotations . There was a considerable increase in the number of transcripts captured by the annotation for ARS-UI_Ramb_v2.0 60,064) relative to Oar_Rambouillet_v1.0 42,058) and the liftover annotation (Ramb1LO2)   lifting over the annotation for Oar_Rambouillet_v1.0 onto ARS-UI_Ramb_v2.0. According to the annotation report provided by NCBI , 70% of The ARS-UI_Ramb_v2.0 genome assembly serves as a reference for genetic investigation of traits important in sheep research and production across the world. This genome is assembled from the same animal used in the Ovine FAANG Project, which provides a high-quality basis for epigenetic annotation to serve the international sheep genomics community and scientific community at large.https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Ovis_aries/104/. Ovis aries Annotation Release 104 is also available in RefSeq and other NCBI genome resources [The datasets supporting the results of this article are available in the RefSeq repository, GCF_016772045.1, and in the GigaScience Database . RNA seqesources .Supplementary File 1. Annotation lifted over from Oar_rambouillet_v1.0 to ARS-UI_Ramb_v2.0.Supplementary File 2. RNA file from annotation lift over.Supplementary File 3. Scripts from annotation lift over.giab096_GIGA-D-21-00165_Original_SubmissionClick here for additional data file.giab096_GIGA-D-21-00165_Revision_1Click here for additional data file.giab096_GIGA-D-21-00165_Revision_2Click here for additional data file.giab096_Response_to_Reviewer_Comments_Revision_1Click here for additional data file.giab096_Reviewer_1_Report_Original_SubmissionAaron Shafer -- 8/30/2021 ReviewedClick here for additional data file.giab096_Reviewer_2_Report_Original_SubmissionElizabeth Ross -- 10/10/2021 ReviewedClick here for additional data file.giab096_Supplemental_FilesClick here for additional data file.bp: base pairs; BUSCO: Benchmarking Universal Single-Copy Orthologs; CAGE: cap analysis gene expression; EDTA: ethylenediaminetetraacetic acid; FAANG: Ovine Functional Annotation of Animal Genomes; Gb: gigabase pairs; kb: kilobase pairs; Mb: megabase pairs; NCBI: National Center for Biotechnology Information; PacBio: Pacific Biosciences; SDS: sodium dodecyl sulfate; SNP: single-nucletide polymorphism; USDA: United States Department of Agriculture; WGS: whole-genome shotgun.Funding was provided by Agriculture and Food Research Initiative Competitive grants from the USDA National Institute of Food and Agriculture supporting improvements of the sheep genomes (2013–67015-21228) and FAANG activities . Additional funding was received from the International Sheep Genome Consortium (217201191442) and infrastructure support from a grant to R. Gibbs from the NIH NHGRI Large-Scale Sequencing Program (U54 HG003273).D.M.B. was supported by appropriated USDA CRIS project 5090–31000-026–00-D. T.P.L.S. was supported by appropriated USDA CRIS Project 3040–31000-100–00D. B.D.R. was supported by appropriated USDA CRIS Project 8042–31000-001–00-D. The USDA does not endorse any products or services. Mentioning of trade names is for information purposes only. The USDA is an equal opportunity employer.B.M.M., T.P.L.S., D.M.B., and B.D.R. conceptualized the study. B.M.M., N.E.C., M.P.H., and T.P.L.S. selected the animal and collected samples. K.W. and S.C.M. facilitated the generation of RSII, short-read, and Hi-C data. T.P.L.S. facilitated the nanopore long-read data generation. K.M.D., D.M.B., T.P.L.S., B.M.M., and B.D.R. performed the genome assembly, scaffolding, RNA-sequencing alignment, polishing, and quality control. M.S. and E.L.C. contributed the section describing the LiftOff annotation and comparative analysis of transcript expression estimates for the 3 annotations. K.M.D., D.M.B., T.P.L.S., B.M.M., and B.D.R. generated tables and figures and drafted the manuscript. K.M.D., D.M.B., K.W., S.C.M., N.E.C., T.P.L.S., B.M.M., and B.D.R. edited the manuscript. All authors contributed to the article and approved the final version.The authors have no conflicts of interest.