T

Tim Fennell

Fulcrum Therapeutics (United States)

Publishes on Genomics and Phylogenetic Studies, Genomics and Rare Diseases, CRISPR and Genetic Engineering. 29 papers and 101k citations.

29Publications
101kTotal Citations

Is this you? Claim your profile.

Add your photo, update your bio, and get notified when your ranking changes.

Top publicationsby citations

The Sequence Alignment/Map format and SAMtools
Heng Li, Bob Handsaker, Alec Wysoker et al.|Bioinformatics|2009
Cited by 67.1kOpen Access

SUMMARY: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. AVAILABILITY: http://samtools.sourceforge.net.

Scaling accurate genetic variant discovery to tens of thousands of samples
Ryan Poplin, Valentín Ruano-Rubio, Mark A. DePristo et al.|bioRxiv (Cold Spring Harbor Laboratory)|2017
Cited by 2.1kOpen Access

Abstract Comprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel assembly-based approach to variant calling, the GATK HaplotypeCaller (HC) and Reference Confidence Model (RCM), that determines genotype likelihoods independently per-sample but performs joint calling across all samples within a project simultaneously. We show by calling over 90,000 samples from the Exome Aggregation Consortium (ExAC) that, in contrast to other algorithms, the HC-RCM scales efficiently to very large sample sizes without loss in accuracy; and that the accuracy of indel variant calling is superior in comparison to other algorithms. More importantly, the HC-RCM produces a fully squared-off matrix of genotypes across all samples at every genomic position being investigated. The HC-RCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.