HapTree-X: An Integrative Bayesian Framework for Haplotype Reconstruction from Transcriptome and Genome Sequencing Data

Berger, Emily; Yörükoğlu, Deniz; Berger, Bonnie

doi:10.1007/978-3-319-16706-0_4

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…The phasing of rare and de novo variants is crucial for identifying putative causal variants in medical genetics, for example by distinguishing compound heterozygotes from two variants on the same allele. Existing methods to phase variants include phasing by transmission 1 , only available in familial studies, population based phasing 2 3 , which is ineffective for rare and de novo variants, phasing by sequencing long genomic fragments 4 5 , which requires specialized and costly technology, and phasing using expression data by inferring haplotype through allelic imbalance 6 , which only applies to loci with well-detected allelic expression 7 . An alternative approach termed ‘read backed phasing' uses readily available short read DNA-seq 8 9 10 ; however it is limited by the relatively short distances which can be spanned by the reads.…”

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confidence: 99%

Rare variant phasing and haplotypic expression from RNA sequencing with phASER

et al. 2016

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Haplotype phasing of genetic variants is important for clinical interpretation of the genome, population genetic analysis and functional genomic analysis of allelic activity. Here we present phASER, an accurate approach for phasing variants that are overlapped by sequencing reads, including those from RNA sequencing (RNA-seq), which often span multiple exons due to splicing. Using diverse RNA-seq data we demonstrate that this provides more accurate phasing of rare variants compared with population-based phasing and allows phasing of variants in the same gene up to hundreds of kilobases away that cannot be obtained from DNA sequencing (DNA-seq) reads. We show that in the context of medical genetic studies this improves the resolution of compound heterozygotes. Additionally, phASER provides measures of haplotypic expression that increase power and accuracy in studies of allelic expression. In summary, phasing using RNA-seq and phASER is accurate and improves studies where rare variant haplotypes or allelic expression is needed.

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confidence: 99%

Rare variant phasing and haplotypic expression from RNA sequencing with phASER

et al. 2016

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“…To solve polyploid phasing problems, the maximum likelihood framework is a common algorithmic strategy. HapTree [ 50 , 87 ] uses the relative likelihood algorithm to identify k-ploidy phasing for first n SNPs, that is conditioned on previous n -1 SNPs. This approach lays the first theoretical foundation of polyploidy phasing problems.…”

Section: Reference-based Haplotype Reconstructionmentioning

confidence: 99%

Computational methods for chromosome-scale haplotype reconstruction

Garg

2021

Genome Biol

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High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.

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“…While some of these optimizations have been applied individually, no framework is currently able to seamlessly integrate them under a single, easy-to-use interface. To this end, we provide use cases where Seq improves on current approaches by re-implementing a myriad of popular genomics applications in the language, spanning tasks such as finding super-maximal exact matches (BWA-MEM [22]), genome homology table construction (CORA [38]), Hamming distance-based all-mapping (mrsFAST [16]), long-read alignment (minimap2 [23]), SAM/BAM preprocessing (GATK [26]), global sequence alignment (AVID [10]) and haplotype phasing (HapTree [9,8]). We show that Seq's compiler optimizations result in substantial performance improvements over these tools, including a 2× improvement in FM-index querying, up to a 4.5× improvement in Smith-Waterman alignment, up to a 3× improvement in Hamming-distance based read mapping, and up to an order of magnitude improvement in data preprocessing, genome index construction and numerical downstream analysis ( Table 1)-all without loss of accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Python-based optimization framework for high-performance genomics

Shajii

Numanagić

et al. 2020

Preprint

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Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq--- a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

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HapTree-X: An Integrative Bayesian Framework for Haplotype Reconstruction from Transcriptome and Genome Sequencing Data

Cited by 8 publications

References 3 publications

Rare variant phasing and haplotypic expression from RNA sequencing with phASER

Rare variant phasing and haplotypic expression from RNA sequencing with phASER

Computational methods for chromosome-scale haplotype reconstruction

A Python-based optimization framework for high-performance genomics

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