Biology subjects

Sarkar, H.

Publications and source records attributed to Sarkar, H..

24 records · Page 2Linked to original sources

bioRxiv2020

A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification

MotivationDroplet based single cell RNA-seq (dscRNA-seq) data is being generated at an unprecedented pace, and the accurate estimation of gene level abundances for each cell is a crucial first step in most dscRNA-seq analyses. When preprocessing the raw dscRNA-seq data to generate a count matrix, care must be taken to account for the potentially large number of multi-mapping locations per read. The sparsity of dscRNA-seq data, and the strong 3 sampling bias, makes it difficult to disambiguate cases where there is no uniquely mapping read to any of the candidate target genes. ResultsWe introduce a Bayesian framework for information sharing across cells within a sample, or across multiple modalities of data using the same sample, to improve gene quantification estimates for dscRNA-seq data. We use an anchor-based approach to connect cells with similar gene expression patterns, and learn informative, empirical priors which we provide to alevins gene multi-mapping resolution algorithm. This improves the quantification estimates for genes with no uniquely mapping reads (i.e. when there is no unique intra-cellular information). We show our new model improves the per cell gene level estimates and provides a principled framework for information sharing across multiple modalities. We test our method on a combination of simulated and real datasets under various setups. AvailabilityThe information sharing model is included in alevin and is implemented in C++14. It is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/salmon as of version 1.1.0. Contactasrivastava@cs.stonybrook.edu, rob@cs.umd.edu

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bioRxiv2020

Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data

MotivationAdvances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcriptlevel abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects. ResultsWe introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly-reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. AvailabilityTerminus is implemented in Rust, and is freely-available and open-source. It can be obtained from https://github.com/COMBINE-lab/Terminus. Contactrob@cs.umd.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2019

Alignment and mapping methodology influence transcript abundance estimation

BackgroundThe accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.\n\nResultsWe investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.\n\nConclusionWe observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.

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bioRxiv2017

A space and time-efficient index for the compacted colored de Bruijn graph

We present a novel data structure for representing and indexing the compacted colored de Bruijn graph, which allows for efficient pattern matching and retrieval of the reference information associated with each k-mer. As the popularity of the de Bruijn graph as an index has increased over the past few years, so have the number of proposed representations of this structure. Existing structures typically fall into two categories; those that are hashing-based and provide very fast access to the underlying k-mer information, and those that are space-frugal and provide asymptotically efficient but practically slower pattern search.\n\nOur representation achieves a compromise between these two extremes. By building upon minimum perfect hashing, carefully organizing our data structure, and making use of succinct representations where applicable, our data structure provides practically fast k-mer lookup while greatly reducing the space compared to traditional hashing-based implementations. Further, we describe a sampling scheme built on the same underlying representation, which provides the ability to trade off k-mer query speed for a reduction in the de Bruijn graph index size. We believe this representation strikes a desirable balance between speed and space usage, and it will allow for fast search on large reference sequences.\n\nPufferfish is developed in C++11, is open source (GPL v3), and is available at https://github.com/COMBINE-lab/Pufferfish. The scripts used to generate the results in this manuscript are available at https://github.com/COMBINE-lab/pufferfish_experiments.

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bioRxiv2017

Towards Selective-Alignment: Producing Accurate And Sensitive Alignments Using Quasi-Mapping

MotivationWe introduce an algorithm for selectively aligning high-throughput sequencing reads to a transcriptome, with the goal of improving transcript-level quantification. This algorithm attempts to bridge the gap between fast \"mapping\" algorithms and more traditional alignment procedures.\n\nResultsWe adopt a hybrid approach that is able to increase mapping accuracy while still retaining much of the efficiency of fast mapping algorithms. To achieve this, we introduce a new approach that explores the candidate search space with high sensitivity as well as a collection of carefully-engineered heuristics to efficiently filter these candidates. Additionally, unlike the strategies adopted in most aligners which first align the ends of paired-end reads independently, we introduce a notion of co-mapping. This procedure exploits relevant information between the \"hits\" from the left and right ends of paired-end reads before full alignments or mappings for each are generated, which improves the efficiency of filtering likely-spurious alignments. Finally, we demonstrate the utility of selective alignment in improving the accuracy of efficient transcript-level quantification from RNA-seq reads. Specifically, we show that selective-alignment is able to resolve certain complex mapping scenarios that can confound existing fast mapping procedures, while simultaneously eliminating spurious alignments that fast mapping approaches can produce.\n\nAvailabilitySelective-alignment is implemented in C++11 as a part of Salmon, and is available as open source software, under GPL v3, at: https://github.com/COMBINE-lab/salmon/tree/selective-alignment\n\nContactrob.patro@cs.stonybrook.edu

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bioRxiv2016

Quark enables semi-reference-based compression of RNA-seq data

MotivationThe past decade has seen an exponential increase in biological sequencing capacity, and there has been a simultaneous effort to help organize and archive some of the vast quantities of sequencing data that are being generated. While these developments are tremendous from the perspective of maximizing the scientific utility of available data, they come with heavy costs. The storage and transmission of such vast amounts of sequencing data is expensive.\n\nResultsWe present Quark, a semi-reference-based compression tool designed for RNA-seq data. Quark makes use of a reference sequence when encoding reads, but produces a representation that can be decoded independently, without the need for a reference. This allows Quark to achieve markedly better compression rates than existing reference-free schemes, while still relieving the burden of assuming a specific, shared reference sequence between the encoder and decoder. We demonstrate that Quark achieves state-of-the-art compression rates, and that, typically, only a small fraction of the reference sequence must be encoded along with the reads to allow reference-free decompression.\n\nAvailabilityQuark is implemented in C++11, and is available under a GPLv3 license at www.github.com/COMBINE-lab/quark.\n\nContactrob.patro@cs.stonybrook.edu

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