A massively parallel strategy for STR marker development, capture, and genotyping

USAT: a bioinformatic toolkit to facilitate interpretation and comparative visualization of tandem repeat sequences

Background

Tandem repeats (TR), highly variable genomic variants, are widely used in individual identification, disease diagnostics, and evolutionary studies. The recent advances in sequencing technologies and bioinformatic tools facilitate calling TR haplotypes genome widely. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision in characterizing TR haplotypes. The need to identify the differences at the single nucleotide level between or among TR haplotypes with an easy-use bioinformatic tool is essential.

Results

In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which takes TR haplotype output from existing tools to perform allele size conversion, sequence comparison of haplotypes, figure plotting, comparison for allele distribution, and interactive visualization. An exemplary application of USAT for analysis of the CODIS core STR loci for DNA forensics with benchmarking human individuals demonstrated the capabilities of USAT. USAT has user-friendly graphic interfaces and runs fast in major computing operating systems with parallel computing enabled.

Conclusion

USAT is a user-friendly bioinformatics software for interpretation, visualization, and comparisons of TRs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-05021-1.

BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data

Background

Simple Sequence Repeats (SSRs) are short tandem repeats of nucleotide sequences. It has been shown that SSRs are associated with human diseases and are of medical relevance. Accordingly, a variety of computational methods have been proposed to mine SSRs from genomes. Conventional methods rely on a high-quality complete genome to identify SSRs. However, the sequenced genome often misses several highly repetitive regions. Moreover, many non-model species have no entire genomes. With the recent advances of next-generation sequencing (NGS) techniques, large-scale sequence reads for any species can be rapidly generated using NGS. In this context, a number of methods have been proposed to identify thousands of SSR loci within large amounts of reads for non-model species. While the most commonly used NGS platforms (e.g., Illumina platform) on the market generally provide short paired-end reads, merging overlapping paired-end reads has become a common way prior to the identification of SSR loci. This has posed a big data analysis challenge for traditional stand-alone tools to merge short read pairs and identify SSRs from large-scale data.

Results

In this study, we present a new Hadoop-based software program, termed BigFiRSt, to address this problem using cutting-edge big data technology. BigFiRSt consists of two major modules, BigFLASH and BigPERF, implemented based on two state-of-the-art stand-alone tools, FLASH and PERF, respectively. BigFLASH and BigPERF address the problem of merging short read pairs and mining SSRs in the big data manner, respectively. Comprehensive benchmarking experiments show that BigFiRSt can dramatically reduce the execution times of fast read pairs merging and SSRs mining from very large-scale DNA sequence data.

Conclusions

The excellent performance of BigFiRSt mainly resorts to the Big Data Hadoop technology to merge read pairs and mine SSRs in parallel and distributed computing on clusters. We anticipate BigFiRSt will be a valuable tool in the coming biological Big Data era.

New genetic markers for Sapotaceae phylogenomics: More than 600 nuclear genes applicable from family to population levels

Some tropical plant families, such as the Sapotaceae, have a complex taxonomy, which can be resolved using Next Generation Sequencing (NGS). For most groups however, methodological protocols are still missing. Here we identified 531 monocopy genes and 227 Short Tandem Repeats (STR) markers and tested them on Sapotaceae using target capture and NGS. The probes were designed using two genome skimming samples from Capurodendron delphinense and Bemangidia lowryi, both from the Tseboneae tribe, as well as the published Manilkara zapota transcriptome from the Sapotoideae tribe. We combined our probes with 261 additional ones previously published and designed for the entire angiosperm group. On a total of 792 low-copy genes, 638 showed no signs of paralogy and were used to build a phylogeny of the family with 231 individuals from all main lineages. A highly supported topology was obtained at high taxonomic ranks but also at the species level. This phylogeny revealed the existence of more than 20 putative new species. Single nucleotide polymorphisms (SNPs) extracted from the 638 genes were able to distinguish lineages within a species complex and to highlight geographical structuration. STR were recovered efficiently for the species used as reference (C. delphinense) but the recovery rate decreased dramatically with the phylogenetic distance to the focal species. Altogether, the new loci will help reaching a sound taxonomic understanding of the family Sapotaceae for which many circumscriptions and relationships are still debated, at the species, genus and tribe levels.

Population-level inferences from environmental DNA-Current status and future perspectives

Environmental DNA (eDNA) extracted from water samples has recently shown potential as a valuable source of population genetic information for aquatic macroorganisms. This approach offers several potential advantages compared with conventional tissue-based methods, including the fact that eDNA sampling is noninvasive and generally more cost-efficient. Currently, eDNA approaches have been limited to single-marker studies of mitochondrial DNA (mtDNA), and the relationship between eDNA haplotype composition and true haplotype composition still needs to be thoroughly verified. This will require testing of bioinformatic and statistical software to correct for erroneous sequences, as well as biases and random variation in relative sequence abundances. However, eDNA-based population genetic methods have far-reaching potential for both basic and applied research. In this paper, we present a brief overview of the achievements of eDNA-based population genetics to date, and outline the prospects for future developments in the field, including the estimation of nuclear DNA (nuDNA) variation and epigenetic information. We discuss the challenges associated with eDNA samples as opposed to those of individual tissue samples and assess whether eDNA might offer additional types of information unobtainable with tissue samples. Lastly, we provide recommendations for determining whether an eDNA approach would be a useful and suitable choice in different research settings. We limit our discussion largely to contemporary aquatic systems, but the advantages, challenges, and perspectives can to a large degree be generalized to eDNA studies with a different spatial and temporal focus.

SONiCS: PCR stutter noise correction in genome-scale microsatellites

Motivation

Massively parallel capture of short tandem repeats (STRs, or microsatellites) provides a strategy for population genomic and demographic analyses at high resolution with or without a reference genome. However, the high Polymerase Chain Reaction (PCR) cycle numbers needed for target capture experiments create genotyping noise through polymerase slippage known as PCR stutter.

Results

We developed SONiCS-Stutter mONte Carlo Simulation-a solution for stutter correction based on dense forward simulations of PCR and capture experimental conditions. To test SONiCS, we genotyped a 2499-marker STR panel in 22 humpback dolphins (Sousa sahulensis) using target capture, and generated capillary-based genotypes to validate five of these markers. In these 110 comparisons, SONiCS showed a 99.1% accuracy rate and a 98.2% genotyping success rate, miscalling a single allele in a marker with low sequence coverage and rejecting another as un-callable.

Availability and implementation

Source code and documentation for SONiCS is freely available at https://github.com/kzkedzierska/sonics. Raw read data used in experimental validation of SONiCS have been deposited in the Sequence Read Archive under accession number SRP135756.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic and genomic monitoring with minimally invasive sampling methods

The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD-seq) require large amounts of high-quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g., collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low-quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next-generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.