Avi Srivastava
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Explore the profile of Avi Srivastava including associated specialties, affiliations and a list of published articles.
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21
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6720
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Recent Articles
11.
Srivastava A, Malik L, Sarkar H, Zakeri M, Almodaresi F, Soneson C, et al.
Genome Biol
. 2020 Sep;
21(1):239.
PMID: 32894187
Background: The 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...
12.
Srivastava A, Malik L, Sarkar H, Patro R
Bioinformatics
. 2020 Jul;
36(Suppl_1):i292-i299.
PMID: 32657394
Motivation: Droplet-based single-cell RNA-seq (dscRNA-seq) data are being generated at an unprecedented pace, and the accurate estimation of gene-level abundances for each cell is a crucial first step in most...
13.
Sarkar H, Srivastava A, Corrada Bravo H, Love M, Patro R
Bioinformatics
. 2020 Jul;
36(Suppl_1):i102-i110.
PMID: 32657377
Motivation: Advances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the...
14.
Sarkar H, Srivastava A, Patro R
Bioinformatics
. 2019 Sep;
35(14):i136-i144.
PMID: 31510649
Summary: With the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data...
15.
Zhu A, Srivastava A, Ibrahim J, Patro R, Love M
Nucleic Acids Res
. 2019 Aug;
47(18):e105.
PMID: 31372651
A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the...
16.
Srivastava A, Malik L, Smith T, Sudbery I, Patro R
Genome Biol
. 2019 Mar;
20(1):65.
PMID: 30917859
We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell...
17.
Pandrekar S, Chen X, Gopalkrishna G, Srivastava A, Saltz M, Saltz J, et al.
AMIA Annu Symp Proc
. 2019 Mar;
2018:867-876.
PMID: 30815129
Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns...
18.
Almodaresi F, Sarkar H, Srivastava A, Patro R
Bioinformatics
. 2018 Jun;
34(13):i169-i177.
PMID: 29949982
Motivation: Indexing reference sequences for search-both individual genomes and collections of genomes-is an important building block for many sequence analysis tasks. Much work has been dedicated to developing full-text indices...
19.
Zakeri M, Srivastava A, Almodaresi F, Patro R
Bioinformatics
. 2017 Sep;
33(14):i142-i151.
PMID: 28881996
Motivation: Many methods for transcript-level abundance estimation reduce the computational burden associated with the iterative algorithms they use by adopting an approximate factorization of the likelihood function they optimize. This...
20.
Srivastava A, Sarkar H, Gupta N, Patro R
Bioinformatics
. 2016 Jun;
32(12):i192-i200.
PMID: 27307617
Motivation: The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is...