Dataset Information

HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data.

ABSTRACT:

Motivation

Microsatellites are among the most useful genetic markers in population biology. High-throughput sequencing of microsatellite-enriched libraries dramatically expedites the traditional process of screening recombinant libraries for microsatellite markers. However, sorting through millions of reads to distill high-quality polymorphic markers requires special algorithms tailored to tolerate sequencing errors in locus reconstruction, distinguish paralogous loci, rarify raw reads originating from the same amplicon and sort out various artificial fragments resulting from recombination or concatenation of auxiliary adapters. Existing programs warrant improvement.

Results

We describe a microsatellite prediction framework named HighSSR for microsatellite genotyping based on high-throughput sequencing. We demonstrate the utility of HighSSR in comparison to Roche gsAssembler on two Roche 454 GS FLX runs. The majority of the HighSSR-assembled loci were reliably mapped against model organism reference genomes. HighSSR demultiplexes pooled libraries, assesses locus polymorphism and implements Primer3 for the design of PCR primers flanking polymorphic microsatellite loci. As sequencing costs drop and permit the analysis of all project samples on next-generation platforms, this framework can also be used for direct simple sequence repeats genotyping.

Availability

http://code.google.com/p/highssr/

SUBMITTER: Churbanov A

PROVIDER: S-EPMC4439519 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Publications

HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data.

Churbanov Alexander A Ryan Rachael R Hasan Nabeeh N Bailey Donovan D Chen Haofeng H Milligan Brook B Houde Peter P

Bioinformatics (Oxford, England) 20120906 21

<h4>Motivation</h4>Microsatellites are among the most useful genetic markers in population biology. High-throughput sequencing of microsatellite-enriched libraries dramatically expedites the traditional process of screening recombinant libraries for microsatellite markers. However, sorting through millions of reads to distill high-quality polymorphic markers requires special algorithms tailored to tolerate sequencing errors in locus reconstruction, distinguish paralogous loci, rarify raw reads o ...[more]

PMID: 22954626

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Project description:Background Y-chromosome DNA (Y-DNA) has been used for tracing paternal lineages and offers a clear path from an individual to a known, or likely, direct paternal ancestor. The advance of next-generation sequencing (NGS) technologies increasingly improves the resolution of the non-recombining region of the Y-chromosome (NRY). However, a lack of suitable computer tools prevents the use of NGS data from the Y-DNA studies. Results We developed Y-LineageTracker, a high-throughput analysis framework that not only utilizes state-of-the-art methodologies to automatically determine NRY haplogroups and identify microsatellite variants of Y-chromosome on a fine scale, but also optimizes comprehensive Y-DNA analysis methods for NGS data. Notably, Y-LineageTracker integrates the NRY haplogroup and Y-STR analysis modules with recognized strategies to robustly suggest an interpretation for paternal genetics and evolution. NRY haplogroup module mainly covers haplogroup classification, clustering analysis, phylogeny construction, and divergence time estimation of NRY haplogroups, and Y-STR module mainly includes Y-STR genotyping, statistical calculation, network analysis, and estimation of time to the most recent common ancestor (TMRCA) based on Y-STR haplotypes. Performance comparison indicated that Y-LineageTracker outperformed existing Y-DNA analysis tools for the high performance and satisfactory visualization effect. Conclusions Y-LineageTracker is an open-source and user-friendly command-line tool that provide multiple functions to efficiently analyze Y-DNA from NGS data at both Y-SNP and Y-STR level. Additionally, Y-LineageTracker supports various formats of input data and produces high-quality figures suitable for publication. Y-LineageTracker is coded with Python3 and supports Windows, Linux, and macOS platforms, and can be installed manually or via the Python Package Index (PyPI). The source code, examples, and manual of Y-LineageTracker are freely available at https://www.picb.ac.cn/PGG/resource.php or CodeOcean (https://codeocean.com/capsule/7424381/tree). Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04057-z.

Project description:BackgroundHost genetic backgrounds affect gene functions. The genetic backgrounds of genetically engineered organisms must be identified to confirm their genetic backgrounds identity with those of recipients. Marker-assisted backcrossing (MAB), transgenesis and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) editing are three commonly used genetic engineering techniques. However, methods for genetic background screening between genetically engineered organisms and corresponding recipients suffer from low efficiency, low accuracy or high cost.ResultsHere, we improved our previously reported AmpSeq-SSR method, an amplicon sequencing-based simple sequence repeat (SSR) genotyping method, by selecting SSR loci with high polymorphism among varieties. Ultimately, a set of 396 SSRs was generated and applied to evaluate the genetic backgrounds identity between rice lines developed through MAB, transgenesis, and CRISPR/Cas9 editing and the respective recipient rice. We discovered that the percentage of different SSRs between the MAB-developed rice line and its recipient was as high as 23.5%. In contrast, only 0.8% of SSRs were different between the CRISPR/Cas9-system-mediated rice line and its recipient, while no SSRs showed different genotypes between the transgenic rice line and its recipient. Furthermore, most differential SSRs induced by MAB technology were located in non-coding regions (62.9%), followed by untranslated regions (21.0%) and coding regions (16.1%). Trinucleotide repeats were the most prevalent type of altered SSR. Most importantly, all altered SSRs located in coding regions were trinucleotide repeats.ConclusionsThis method is not only useful for the background evaluation of genetic resources but also expands our understanding of the unintended effects of different genetic engineering techniques. While the work we present focused on rice, this method can be readily extended to other organisms.

Dataset Information

HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data.

Motivation

Results

Availability

Publications

HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data.

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