Biology subjects

Bejerano, G.

Publications and source records attributed to Bejerano, G..

24 records · Page 2Linked to original sources

bioRxiv2017

Comment on "A genetic signature of the evolution of loss of flight in the Galapagos cormorant"

Burga et al. could not identify gene regulatory regions which might contribute to flightlessness in the Galapagos cormorant. Using a bird-specific alignment we discover 48 limb enhancers showing strong accelerated evolution in P. harrisi, including enhancers of key cilia development, hedgehog signaling, and planar cell polarity genes, such as Prickle1, Twist2 and Ndk1, extending Burga's proposed mechanism into the non-coding genome.

genomics↗

bioRxiv2017

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Experimental detection of RNA splicing branchpoints, the nucleotide serving as the nucleophile in the first catalytic step of splicing, is difficult. To date, annotations exist for only 16-21% of 3 splice sites in the human genome and even these limited annotations have been shown to be plagued by noise. We develop a sequence-only, deep learning based branchpoint predictor, LaBranchoR, which we conclude predicts a correct branchpoint for over 90% of 3 splice sites genome-wide. Our predicted branchpoints show large agreement with trends observed in the raw data, but analysis of conservation signatures and overlap with pathogenic variants reveal that our predicted branchpoints are generally more reliable than the raw data itself. We use our predicted branchpoints to identify a sequence element upstream of branchpoints consistent with extended U2 snRNA base pairing, show an association between weak branchpoints and alternative splicing, and explore the effects of variants on branchpoints.

bioinformatics↗

bioRxiv2017

AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE (Automatic Mendelian Literature Evaluation) greatly automates this process. AMELIE parses hundreds of thousands of full text articles to find an underlying diagnosis to explain a patients phenotypes given the patients exome. AMELIE prioritizes patient candidate genes for their likelihood of causing the patients phenotypes. Diagnosis of singleton patients (without relatives exomes) is the most time-consuming scenario. AMELIEs gene ranking method was tested on 215 singleton Mendelian patients with a clinical diagnosis. AMELIE ranked the causal gene among the top 2 in the majority (63%) of cases. Examining AMELIEs top 10 genes, amounting to 8% of 124 candidate genes with rare functional variants per patient, results in diagnosis for 95% of cases. Strikingly, training only on gene pathogenicity knowledge from 2011 leads to identical performance compared to training on current data. An accompanying analysis web portal has launched at AMELIE.stanford.edu.

genetics↗

bioRxiv2017

A novel unbiased test for molecular convergent evolution and discoveries in echolocating, aquatic and high-altitude mammals

Distantly related species entering similar biological niches often adapt by evolving similar morphological and physiological characters. The extent to which genomic molecular convergence, and the extent to which coding mutations underlie this convergent phenotypic evolution remain unknown. Using a novel test, we ask which group of functionally coherent genes is most affected by convergent amino acid substitutions between phenotypically convergent lineages. This most affected sets reveals 75 novel coding convergences in important genes that pattern a highly adapted organ: the cochlea, skin and lung in echolocating, aquatic and high-altitude mammals, respectively. Our test explicitly requires the enriched converged term to not be simultaneously enriched for divergent mutations, and correctly dismisses relaxation-based signals, such as those produced by vision genes in subterranean mammals. This novel test can be readily applied to birds, fish, flies, worms etc., to discover more of the fascinating contribution of protein coding convergence to phenotype convergence.

evolutionary biology↗

bioRxiv2017

Revealing the causative variant in Mendelian patient genomes without revealing patient genomes

Given the rapidly growing utility of critical health information revealed in the human genome, secure genomic computation is essential to moving forward, especially as genome sequencing becomes commonplace. We devise and implement proof-of-principle computational operations for precisely identifying causal variants in Mendelian patients using secure multiparty computation methods based on Yaos protocol. We show multiple real scenarios (small patient cohorts, trio analysis, two hospital collaboration) where the causal variant is discovered jointly, while keeping up to 99.7% of all participants most sensitive genomic information private. All similar operations performed today to diagnose such cases are done openly, keeping 0% of participants genomic information private. Our work will help usher in an era where genomes can be both utilized and truly protected.

genomics↗

bioRxiv2017

Darwin: A Hardware-acceleration Framework for Genomic Sequence Alignment

Genomics is set to transform medicine and our understanding of life in fundamental ways. But the growth in genomics data has been overwhelming - far outpacing Moores Law. The advent of third generation sequencing technologies is providing new insights into genomic contribution to diseases with complex mutation events, but have prohibitively high computational costs. Over 1,300 CPU hours are required to align reads from a 54x coverage of the human genome to a reference (estimated using [1]), and over 15,600 CPU hours to assemble the reads de novo [2]. This paper proposes \"Darwin\" - a hardware-accelerated framework for genomic sequence alignment that, without sacrificing sensitivity, provides 125x and 15.6x speedup over the state-of-the-art software counterparts for reference-guided and de novo assembly of third generation sequencing reads, respectively. For pairwise alignment of sequences, Darwin is over 39,000x more energy-efficient than software. Darwin uses (i) a novel filtration strategy, called D-SOFT, to reduce the search space for sequence alignment at high speed, and (ii) a hardware-accelerated version of GACT, a novel algorithm to generate near-optimal alignments of arbitrarily long genomic sequences using constant memory for trace-back. Darwin is adaptable, with tunable speed and sensitivity to match emerging sequencing technologies and to meet the requirements of genomic applications beyond read assembly.

genomics↗