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Accurate assembly of transcripts through phase-preserving graph decomposition.

ABSTRACT: We introduce Scallop, an accurate reference-based transcript assembler that improves reconstruction of multi-exon and lowly expressed transcripts. Scallop preserves long-range phasing paths extracted from reads, while producing a parsimonious set of transcripts and minimizing coverage deviation. On 10 human RNA-seq samples, Scallop produces 34.5% and 36.3% more correct multi-exon transcripts than StringTie and TransComb, and respectively identifies 67.5% and 52.3% more lowly expressed transcripts. Scallop achieves higher sensitivity and precision than previous approaches over a wide range of coverage thresholds.

SUBMITTER: Shao M

PROVIDER: S-EPMC5722698 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Accurate assembly of transcripts through phase-preserving graph decomposition.

Shao Mingfu M Kingsford Carl C

Nature biotechnology 20171113 12

We introduce Scallop, an accurate reference-based transcript assembler that improves reconstruction of multi-exon and lowly expressed transcripts. Scallop preserves long-range phasing paths extracted from reads, while producing a parsimonious set of transcripts and minimizing coverage deviation. On 10 human RNA-seq samples, Scallop produces 34.5% and 36.3% more correct multi-exon transcripts than StringTie and TransComb, and respectively identifies 67.5% and 52.3% more lowly expressed transcript ...[more]

PMID: 29131147

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Project description:The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Dataset Information

Accurate assembly of transcripts through phase-preserving graph decomposition.

Publications

Accurate assembly of transcripts through phase-preserving graph decomposition.

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