Phred-Phrap package to analyses tools: a pipeline to facilitate population genetics re-sequencing studies

Abstract

BackgroundTargeted re-sequencing is one of the most powerful and widely used strategies for population genetics studies because it allows an unbiased screening for variation that is suitable for a wide variety of organisms. Examples of studies that require re-sequencing data are evolutionary inferences, epidemiological studies designed to capture rare polymorphisms responsible for complex traits and screenings for mutations in families and small populations with high incidences of specific genetic diseases. Despite the advent of next-generation sequencing technologies, Sanger sequencing is still the most popular approach in population genetics studies because of the widespread availability of automatic sequencers based on capillary electrophoresis and because it is still less prone to sequencing errors, which is critical in population genetics studies. Two popular software applications for re-sequencing studies are Phred-Phrap-Consed-Polyphred, which performs base calling, alignment, graphical edition and genotype calling and DNAsp, which performs a set of population genetics analyses. These independent tools are the start and end points of basic analyses. In between the use of these tools, there is a set of basic but error-prone tasks to be performed with re-sequencing data.ResultsIn order to assist with these intermediate tasks, we developed a pipeline that facilitates data handling typical of re-sequencing studies. Our pipeline: (1) consolidates different outputs produced by distinct Phred-Phrap-Consed contigs sharing a reference sequence; (2) checks for genotyping inconsistencies; (3) reformats genotyping data produced by Polyphred into a matrix of genotypes with individuals as rows and segregating sites as columns; (4) prepares input files for haplotype inferences using the popular software PHASE; and (5) handles PHASE output files that contain only polymorphic sites to reconstruct the inferred haplotypes including polymorphic and monomorphic sites as required by population genetics software for re-sequencing data such as DNAsp.ConclusionWe tested the pipeline in re-sequencing studies of haploid and diploid data in humans, plants, animals and microorganisms and observed that it allowed a substantial decrease in the time required for sequencing analyses, as well as being a more controlled process that eliminates several classes of error that may occur when handling datasets. The pipeline is also useful for investigators using other tools for sequencing and population genetics analyses.

The population genetics of quechuas, the largest native south american group: Autosomal sequences, SNPs, and microsatellites evidence high level of diversity

“…and run on automated capillary sequencers ABI 3130. Sequences were analyzed following the pipeline described in Machado et al (2011). Only 1.3% of genotype calls in polymorphic nucleotide positions contained missing data.…”

Genomic and Enzymatic Results Show Bacillus cellulosilyticus Uses a Novel Set of LPXTA Carbohydrases to Hydrolyze Polysaccharides

“…We converted the initial 454 assembly into a phrap assembly by making fake reads from the consensus, collecting the read pairs in the 454 paired end library. The Phred/Phrap/Consed software package (www.phrap.com) was used for sequence assembly and quality assessment [50], [51], [52] in the following finishing process. After the shotgun stage, reads were assembled with parallel phrap (High Performance Software, LLC).…”

Population, Epidemiological, and Functional Genetics of Gastric Cancer Candidate Genes in Peruvians with Predominant Amerindian Ancestry

“…For comparison of population genetic diversity, we used the re-sequencing data for the same genomic regions in 24 Africans and 23 Europeans from the SeattleSNPs database. We treated the sequences using the pipeline by Machado et al [17]. The following population genetic analyses were performed using the method and software indicated in parentheses: Hardy-Weinberg equilibrium (http://code.google.com/p/glu-genetics/, GLU), genetic diversity and neutrality tests ( [18], DNAsp), haplotypes inferences ( [19], PHASE), and linkage disequilibrium ( [20], Haploview).…”