Dataset Information

Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence.

ABSTRACT: BACKGROUND:Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms. RESULTS:We addressed this gap in resource and presented a computational method and software implementations called CHERIPIC (Computing Homozygosity Enriched Regions In genomes to Prioritise Identification of Candidate variants). We have successfully validated implementation of CHERIPIC using three different types of bulk segregant sequence data from Arabidopsis, maize and barley, respectively. CONCLUSIONS:CHERIPIC allows users to rapidly analyse bulk segregant sequence data and we have made it available as a pre-packaged binary with all dependencies for Linux and MacOS and as Galaxy tool.

SUBMITTER: Rallapalli G

PROVIDER: S-EPMC6323790 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence.

Rallapalli Ghanasyam G Corredor-Moreno Pilar P Chalstrey Edward E Page Martin M MacLean Daniel D

BMC bioinformatics 20190107 1

<h4>Background</h4>Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms.<h4>Results</h4>We addresse ...[more]

PMID: 30616525

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Project description:MotivationA plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures.ResultsWe propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer.AvailabilitySource codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.

Dataset Information

Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence.

Publications

Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence.

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