More Like this

1. CHIIMP : An automated high‐throughput microsatellite genotyping platform reveals greater allelic diversity in wild chimpanzees

   https://doi.org/10.1002/ece3.4302  

   Barbian, Hannah J. ; Connell, Andrew Jesse ; Avitto, Alexa N. ; Russell, Ronnie M. ; Smith, Andrew G. ; Gundlapally, Madhurima S. ; Shazad, Alexander L. ; Li, Yingying ; Bibollet‐Ruche, Frederic ; Wroblewski, Emily E. ; et al ( July 2018 , Ecology and Evolution)

   Short tandem repeats (STRs), also known as microsatellites, are commonly used to noninvasively genotype wild‐living endangered species, including African apes. Until recently, capillary electrophoresis has been the method of choice to determine the length of polymorphicSTRloci. However, this technique is labor intensive, difficult to compare across platforms, and notoriously imprecise. Here we developed a MiSeq‐based approach and tested its performance using previously genotyped fecal samples from long‐term studied chimpanzees in Gombe National Park, Tanzania. Using data from eight microsatellite loci as a reference, we designed a bioinformatics platform that converts raw MiSeq reads into locus‐specific files and automatically calls alleles after filtering stutter sequences and otherPCRartifacts. Applying this method to the entire Gombe population, we confirmed previously reported genotypes, but also identified 31 new alleles that had been missed due to sequence differences and size homoplasy. The new genotypes, which increased the allelic diversity and heterozygosity in Gombe by 61% and 8%, respectively, were validated by replicate amplification and pedigree analyses. This demonstrated inheritance and resolved one case of an ambiguous paternity. Using both singleplex and multiplex locus amplification, we also genotyped fecal samples from chimpanzees in the Greater Mahale Ecosystem in Tanzania, demonstrating the utility of the MiSeq‐based approach for genotyping nonhabituated populations and performing comparative analyses across field sites. The new automated high‐throughput analysis platform (available athttps://github.com/ShawHahnLab/chiimp) will allow biologists to more accurately and effectively determine wildlife population size and structure, and thus obtain information critical for conservation efforts.

    

   more » « less
2. Detecting genomic deletions from high-throughput sequence data with unsupervised learning

   https://doi.org/10.1186/s12859-023-05139-w  

   Li, Xin ; Wu, Yufeng ( January 2023 , BMC Bioinformatics)

   Structural variation (SV), which ranges from 50 bp to$$\sim$$$\sim$ 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. Three types of signals, including discordant read-pairs, reads depth and split reads, are commonly used for SV detection from high-throughput sequence data. Many tools have been developed for detecting SVs by using one or multiple of these signals.

   In this paper, we develop a new method called EigenDel for detecting the germline submicroscopic genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates, and then it clusters similar candidates by using unsupervised learning methods. After that, EigenDel uses a carefully designed approach for calling true deletions from each cluster. We conduct various experiments to evaluate the performance of EigenDel on low coverage sequence data.

   Our results show that EigenDel outperforms other major methods in terms of improving capability of balancing accuracy and sensitivity as well as reducing bias. EigenDel can be downloaded fromhttps://github.com/lxwgcool/EigenDel.

    

   more » « less
3. A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data

   https://doi.org/10.1186/s12859-021-04265-7  

   Zhang, Miao ; Liu, Yiwen ; Zhou, Hua ; Watkins, Joseph ; Zhou, Jin ( June 2021 , BMC Bioinformatics)

   Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce to analyze population structure of low-depth sequencing data.

   The method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common.

   We apply to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The package is available onhttps://github.com/yiwenstat/MCPCA_PopGen.

    

   more » « less
4. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

   https://doi.org/10.1186/s40168-021-01213-8  

   Zhou, Zhichao ; Tran, Patricia Q. ; Breister, Adam M. ; Liu, Yang ; Kieft, Kristopher ; Cowley, Elise S. ; Karaoz, Ulas ; Anantharaman, Karthik ( February 2022 , Microbiome)

   Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

   We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

   METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 athttps://github.com/AnantharamanLab/METABOLIC.

    

   more » « less
5. Plastid Genome Assembly Using Long‐read data

   https://doi.org/10.1111/1755-0998.13787  

   Zhou, Wenbin ; Armijos, Carolina E. ; Lee, Chaehee ; Lu, Ruisen ; Wang, Jeremy ; Ruhlman, Tracey A. ; Jansen, Robert K. ; Jones, Alan M. ; Jones, Corbin D. ( April 2023 , Molecular Ecology Resources)

   Although plastid genome (plastome) structure is highly conserved across most seed plants, investigations during the past two decades have revealed several disparately related lineages that experienced substantial rearrangements. Most plastomes contain a large inverted repeat and two single‐copy regions, and a few dispersed repeats; however, the plastomes of some taxa harbour long repeat sequences (>300 bp). These long repeats make it challenging to assemble complete plastomes using short‐read data, leading to misassemblies and consensus sequences with spurious rearrangements. Single‐molecule, long‐read sequencing has the potential to overcome these challenges, yet there is no consensus on the most effective method for accurately assembling plastomes using long‐read data. We generated a pipeline,plastidGenomeAssemblyUsingLong‐read data (ptGAUL), to address the problem of plastome assembly using long‐read data from Oxford Nanopore Technologies (ONT) or Pacific Biosciences platforms. We demonstrated the efficacy of the ptGAUL pipeline using 16 published long‐read data sets. We showed that ptGAUL quickly produces accurate and unbiased assemblies using only ~50× coverage of plastome data. Additionally, we deployed ptGAUL to assemble four newJuncus(Juncaceae) plastomes using ONT long reads. Our results revealed many long repeats and rearrangements inJuncusplastomes compared with basal lineages of Poales. The ptGAUL pipeline is available on GitHub:https://github.com/Bean061/ptgaul.

    

   more » « less