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Jeremiah A. Wala

dblp:31/9829 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2017
0000-0001-6591-1620ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
sequence alignment
0.312017
SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly · Bioinform. 2017
Bioinformatics and computational biology
sequence analysis
0.312017
SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly · Bioinform. 2017
Bioinformatics and computational biology › sequence analysis
sequence assembly
0.312017
SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly · Bioinform. 2017
Bioinformatics and computational biology › sequence analysis › sequencing data analysis
read filtering
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016
Bioinformatics and computational biology › bioinformatics infrastructure
sequencing data compression
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016
Bioinformatics and computational biology › sequence analysis
sequencing data processing
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

fermi · 0.3HTSlib · 0.3BWA-MEM · 0.3BLAT · 0.3sequence motif matching · 0.2region-specific filtering rules · 0.2
YearPublicationVenuePosition
2017 SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly
abstract
We present SeqLib, a C ++ API and command line tool that provides a rapid and user-friendly interface to BAM/SAM/CRAM files, global sequence alignment operations and sequence assembly. Four C libraries perform core operations in SeqLib: HTSlib for BAM access, BWA-MEM and BLAT for sequence alignment and Fermi for error correction and sequence assembly. Benchmarking indicates that SeqLib has lower CPU and memory requirements than leading C ++ sequence analysis APIs. We demonstrate an example of how minimal SeqLib code can extract, error-correct and assemble reads from a CRAM file and then align with BWA-MEM. SeqLib also provides additional capabilities, including chromosome-aware interval queries and read plotting. Command line tools are available for performing integrated error correction, micro-assemblies and alignment. Availability and Implementation: SeqLib is available on Linux and OSX for the C ++98 standard and later at github.com/walaj/SeqLib. SeqLib is released under the Apache2 license. Additional capabilities for BLAT alignment are available under the BLAT license. Contact: [email protected] ; [email protected].
Jeremiah A. Wala, Rameen Beroukhim
Bioinform.1
2016 VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules
abstract
UNLABELLED: We developed VariantBam, a C ++ read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis. AVAILABILITY AND IMPLEMENTATION: VariantBam and full documentation are available at github.com/jwalabroad/VariantBam CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jeremiah A. Wala, Cheng-Zhong Zhang, Matthew Meyerson, Rameen Beroukhim
Bioinform.1