Buying in to bioinformatics: an introduction to commercial sequence analysis software

Genetic Characterization and Population Structure of Drug-Resistant Mycobacterium tuberculosis Isolated from Brazilian Patients Using Whole-Genome Sequencing

The present study aimed to determine the genetic diversity of isolates of Mycobacterium tuberculosis (Mtb) from presumed drug-resistant tuberculosis patients from several states of Brazil. The isolates had been submitted to conventional drug susceptibility testing for first- and second-line drugs. Multidrug-resistant (MDR-TB) (54.8%) was the most frequent phenotypic resistance profile, in addition to an important high frequency of pre-extensive resistance (p-XDR-TB) (9.2%). Using whole-genome sequencing (WGS), we characterized 298 Mtb isolates from Brazil. Besides the analysis of genotype distribution and possible correlations between molecular and clinical data, we determined the performance of an in-house WGS pipeline with other online pipelines for Mtb lineages and drug resistance profile definitions. Sub-lineage 4.3 (52%) was the most frequent genotype, and the genomic approach revealed a p-XDR-TB level of 22.5%. We detected twenty novel mutations in three resistance genes, and six of these were observed in eight phenotypically resistant isolates. A cluster analysis of 170 isolates showed that 43.5% of the TB patients belonged to 24 genomic clusters, suggesting considerable ongoing transmission of DR-TB, including two interstate transmissions. The in-house WGS pipeline showed the best overall performance in drug resistance prediction, presenting the best accuracy values for five of the nine drugs tested. Significant associations were observed between suffering from fatal disease and genotypic p-XDR-TB (p = 0.03) and either phenotypic (p = 0.006) or genotypic (p = 0.0007) ethambutol resistance. The use of WGS analysis improved our understanding of the population structure of MTBC in Brazil and the genetic and clinical data correlations and demonstrated its utility for surveillance efforts regarding the spread of DR-TB, hopefully helping to avoid the emergence of even more resistant strains and to reduce TB incidence and mortality rates.

Study of mercury resistance and Fourier transform infrared spectroscopy-based metabolic profiling of a potent Bacillus tropicus strain from forest soil

Mercury (Hg) is a highly toxic heavy metal and Hg-resistant indigenous bacterial isolates may offer a green and cost-effective bioremediation strategy to counter Hg contamination. In this study, a potent Hg-resistant bacterium was isolated from the forest soil of a bird sanctuary. Identification using matrix-assisted laser desorption ionization-time of flight mass spectrometry depicted the isolate as a strain of Bacillus tropicus, validated by morphological, biochemical, and molecular studies. The isolate demonstrated biological Hg removal efficiency and capacity of 50.67% and 19.76 mg g-1 , respectively. The plasmid borne resistance determinant, merA, encoding mercuric reductase, was detected in the bacterium endowing it with effective Hg volatilization and resistance capability. A Fourier-transform infrared spectroscopic comparative metabolic profiling revealed the involvement of various functional groups like -COOH, -CH2 , -OH, PO4 - and so on, resulting in differential spectral patterns of the bacterium both in control and Hg-exposed situations. A temporal variance in metabolic signature was also observed during the early and mid-log phase of growth in the presence of Hg. The bacterium described in this study is the first indigenous Hg-resistant strain isolated from the Uttar Dinajpur region, which could be further explored and exploited as a potent bioresource for Hg remediation.

Factors Affecting the Quality of Bacterial Genomes Assemblies by Canu after Nanopore Sequencing

Long-read sequencing (LRS), like Oxford Nanopore Technologies, is usually associated with higher error rates compared to previous generations. Factors affecting the assembly quality are the integrity of DNA, the flowcell efficiency, and, not least all, the raw data processing. Among LRS-intended de novo assemblers, Canu is highly flexible, with its dozens of adjustable parameters. Different Canu parameters were compared for assembling reads of Salmonellaenterica ser. Bovismorbificans (genome size of 4.8 Mbp) from three runs on MinION (N50 651, 805, and 5573). Two of them, with low quality and highly fragmented DNA, were not usable alone for assembly, while they were successfully assembled when combining the reads from all experiments. The best results were obtained by modifying Canu parameters related to the error correction, such as corErrorRate (exclusion of overlaps above a set error rate, set up at 0.40), corMhapSensitivity (the coarse sensitivity level, set to “high”), corMinCoverage (set to 0 to correct all reads, regardless the overlaps length), and corOutCoverage (corrects the longest reads up to the imposed coverage, set to 100). This setting produced two contigs corresponding to the complete sequences of the chromosome and a plasmid. The overall results highlight the importance of a tailored bioinformatic analysis.

SaAlign: Multiple DNA/RNA Sequence Alignment and Phylogenetic Tree Construction Tool for Ultra-large Datasets and Ultra-long Sequences Based on Suffix Array

Multiple DNA/RNA sequence alignment is an important fundamental tool in bioinformatics, especially for phylogenetic tree construction. With DNA-sequencing improvements, the amount of bioinformatics data is constantly increasing, and various tools need to be iterated constantly. Mitochondrial genome analyses of multiple individuals and species require bioinformatics software; therefore, their performances need to be optimized. To improve the alignment of ultra-large datasets and ultra-long sequences, we optimized a dynamic programming algorithm using longest common substring methods. Ultra-large test DNA datasets, containing sequences of different lengths, some over 300 kb (kilobase), revealed that the Multiple DNA/RNA Sequence Alignment Tool Based on Suffix Tree (SaAlign) saved time and computational space. It outperformed the existing technical tools, including MAFFT and HAlign-II. For mitochondrial genome datasets having limited numbers of sequences, MAFFT performed the required tasks, but it could not handle ultra-large mitochondrial genome datasets for core dump error. We implement a multiple DNA/RNA sequence alignment tool based on Center Star strategy and use suffix array algorithm to optimize the spatial and time efficiency. Nowadays, whole-genome research and NGS technology are becoming more popular, and it is necessary to save computational resources for laboratories. That software is of great significance in these aspects, especially in the study of the whole-mitochondrial genome of plants.

JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

Capture enrichment and massively parallel sequencing for human identification

In the past decade, hybridization capture has gained attention within the forensic field for its possible use in human identification. One of the primary benefits to capture enrichment is its applicability to degraded DNA fragments that, due to their reduced size, are not amenable to traditional PCR enrichment techniques. Hybridization capture is typically introduced after genomic library preparation of extracted DNA templates for the subsequent enrichment of mitochondrial DNA or single nucleotide polymorphisms within the nuclear genome. The enriched molecules are then subjected to massively parallel sequencing (MPS) for sensitive and high-throughput DNA sequence generation. Bioinformatic analysis of capture product removes PCR duplicates that were introduced during the laboratory workflow in order to characterize the original DNA template molecules. In the case of aged and degraded skeletal remains, the fraction of endogenous human DNA may be very low; therefore low-coverage sequence analysis may be required. This review contains an overview of current capture methodologies and the primary literature on hybridization capture as evaluated for forensic applications.

GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians

Background

Processing and analyzing whole genome sequencing (WGS) is computationally intense: a single Illumina MiSeq WGS run produces ~ 1 million 250-base-pair reads for each of 24 samples. This poses significant obstacles for smaller laboratories, or laboratories not affiliated with larger projects, which may not have dedicated bioinformatics staff or computing power to effectively use genomic data to protect public health. Building on the success of the cloud-based Galaxy bioinformatics platform (http://galaxyproject.org), already known for its user-friendliness and powerful WGS analytical tools, the Center for Food Safety and Applied Nutrition (CFSAN) at the U.S. Food and Drug Administration (FDA) created a customized ‘instance’ of the Galaxy environment, called GalaxyTrakr (https://www.galaxytrakr.org), for use by laboratory scientists performing food-safety regulatory research. The goal was to enable laboratories outside of the FDA internal network to (1) perform quality assessments of sequence data, (2) identify links between clinical isolates and positive food/environmental samples, including those at the National Center for Biotechnology Information sequence read archive (https://www.ncbi.nlm.nih.gov/sra/), and (3) explore new methodologies such as metagenomics. GalaxyTrakr hosts a variety of free and adaptable tools and provides the data storage and computing power to run the tools. These tools support coordinated analytic methods and consistent interpretation of results across laboratories. Users can create and share tools for their specific needs and use sequence data generated locally and elsewhere.

Results

In its first full year (2018), GalaxyTrakr processed over 85,000 jobs and went from 25 to 250 users, representing 53 different public and state health laboratories, academic institutions, international health laboratories, and federal organizations. By mid-2020, it has grown to 600 registered users and processed over 450,000 analytical jobs. To illustrate how laboratories are making use of this resource, we describe how six institutions use GalaxyTrakr to quickly analyze and review their data. Instructions for participating in GalaxyTrakr are provided.

Conclusions

GalaxyTrakr advances food safety by providing reliable and harmonized WGS analyses for public health laboratories and promoting collaboration across laboratories with differing resources. Anticipated enhancements to this resource will include workflows for additional foodborne pathogens, viruses, and parasites, as well as new tools and services.

Easymap: A User-Friendly Software Package for Rapid Mapping-by-Sequencing of Point Mutations and Large Insertions

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

Depositing annotated sequences in GenBank: there needs to be a better way

Submitting sequences to the National Center for Biotechnology Information (NCBI) is an integral part of research and the publication process for many disciplines within the life sciences, and it will only become more important as sequencing technologies continue to improve. Here, I argue that the available infrastructure and resources for uploading data to NCBI-especially the associated annotations of eukaryotic genomes-are inefficient, hard to use and sometimes just plain bad. This, in turn, is causing some researchers to forgo annotations entirely in their submissions. The time is overdue for the development of sophisticated, user-friendly software for depositing annotated sequences in GenBank.

Raw transcriptomics data to gene specific SSRs: a validated free bioinformatics workflow for biologists

Recent advances in next-generation sequencing technologies have paved the path for a considerable amount of sequencing data at a relatively low cost. This has revolutionized the genomics and transcriptomics studies. However, different challenges are now created in handling such data with available bioinformatics platforms both in assembly and downstream analysis performed in order to infer correct biological meaning. Though there are a handful of commercial software and tools for some of the procedures, cost of such tools has made them prohibitive for most research laboratories. While individual open-source or free software tools are available for most of the bioinformatics applications, those components usually operate standalone and are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures starting from raw sequence data too complicated and time-consuming with the associated learning-curve. Here, we outline a procedure for de novo transcriptome assembly and Simple Sequence Repeats (SSR) primer design solely based on tools that are available online for free use. For validation of the developed workflow, we used Illumina HiSeq reads of different tissue samples of Santalum album (sandalwood), generated from a previous transcriptomics project. A portion of the designed primers were tested in the lab with relevant samples and all of them successfully amplified the targeted regions. The presented bioinformatics workflow can accurately assemble quality transcriptomes and develop gene specific SSRs. Beginner biologists and researchers in bioinformatics can easily utilize this workflow for research purposes.

Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers

Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006 to 2018, including outcomes from a survey measuring attitudes of Pitt researchers about MBIS service and performance.

Genome-wide sequence information reveals recurrent hybridization among diploid wheat wild relatives

Many conflicting hypotheses regarding the relationships among crops and wild species closely related to wheat (the genera Aegilops, Amblyopyrum, and Triticum) have been postulated. The contribution of hybridization to the evolution of these taxa is intensely discussed. To determine possible causes for this, and provide a phylogeny of the diploid taxa based on genome-wide sequence information, independent data were obtained from genotyping-by-sequencing and a target-enrichment experiment that returned 244 low-copy nuclear loci. The data were analyzed using Bayesian, likelihood and coalescent-based methods. D statistics were used to test if incomplete lineage sorting alone or together with hybridization is the source for incongruent gene trees. Here we present the phylogeny of all diploid species of the wheat wild relatives. We hypothesize that most of the wheat-group species were shaped by a primordial homoploid hybrid speciation event involving the ancestral Triticum and Am. muticum lineages to form all other species except Ae. speltoides. This hybridization event was followed by multiple introgressions affecting all taxa except Triticum. Mostly progenitors of the extant species were involved in these processes, while recent interspecific gene flow seems insignificant. The composite nature of many genomes of wheat-group taxa results in complicated patterns of diploid contributions when these lineages are involved in polyploid formation, which is, for example, the case for tetraploid and hexaploid wheats. Our analysis provides phylogenetic relationships and a testable hypothesis for the genome compositions in the basic evolutionary units within the wheat group of Triticeae.

From command-line bioinformatics to bioGUI

Bioinformatics is a highly interdisciplinary field providing (bioinformatics) applications for scientists from many disciplines. Installing and starting applications on the command-line (CL) is inconvenient and/or inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications available to more scientists and, thus, toward a more effective interdisciplinary work. With our bioGUI framework we address two main problems of using CL bioinformatics applications: First, many tools work on UNIX-systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools which, however, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists-even on Windows, due to bioGUI's support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install and use bioinformatics tools with just a few clicks.

Mind the gap: resources required to receive, process and interpret research-returned whole genome data

Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most well known of these new cohort efforts is arguably All of Us, formerly known as the Precision Medicine Cohort Initiative Program. All of Us intends to ascertain at least one million participants in the United States representative of the multiple dimensions of diversity. As an incentive to participate, All of Us is offering the return of research results, including whole genome sequencing data, as well as the opportunity to contribute to the scientific process as non-scientists. The scale and scope of the proposed return of research results are unprecedented. Here, we briefly review possible return of genetic data models, including the likely data file formats and modes of data transfer or access. We also review the resources required to access and interpret the genetic or genomic data once received by the average participant, highlighting the nuanced anticipated barriers that will challenge both the digitally, computationally literate and illiterate participant alike. This inventory of resources required to receive, process, and interpret return of research results exposes the potential for access disparities and warns the scientific community to mind the gap so that all participants have equal access and understanding of the benefits of human genetic research.

Analysis of Genotype Composition of Citrus tristeza virus Populations Using Illumina Miseq Technology

Recent research describing the strain-specific mechanisms underlying experimental CTV superinfection exclusion has far-reaching implications for the manner in which cross-protecting sources should be selected for. The strain composition of both cross-protecting sources and field populations needs to be sufficiently characterized to improve control of severe stem-pitting and decline isolates. Many of the biological, serological, and molecular techniques used in previous studies yield very limited information about the strain composition of populations and the relative titer of their components. In this chapter we describe a protocol for the characterization of CTV populations, based on the use of the next-generation sequencing Illumina MiSeq platform of p33 gene amplicons.

Data challenges of biomedical researchers in the age of omics

BACKGROUND

High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor.

METHODS

A multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach.

RESULTS

The survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e.g., R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis.

CONCLUSIONS

The main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers. The authors identified the positive impact of medical and/or science libraries by establishing bioinformatics support to researchers.

Viruses infecting common bean (Phaseolus vulgaris L.) in Tanzania: A review on molecular characterization, detection and disease management options

Common bean (Phaseolus vulgaris L.) is a major legume crop, serving as a main source of dietary protein and calories and generating income for many Tanzanians. It is produced in nearly all agro-ecological zones of Tanzania. However, the average yields are low (<1000 kg/ha), which is attributed to many factors including virus diseases. The most important viruses of common bean in Tanzania are Bean common mosaic virus (BCMV) and Bean common mosaic necrosis virus (BCMNV) but other viruses have also been reported. There has never been a review of common bean virus diseases in the country, and the lack of collated information makes their management difficult. Therefore, this review focuses on (1) occurrence of different viruses of common bean in Tanzania, (2) molecular characterization of these viruses, (3) detection tools for common bean viruses in Tanzania and (4) available options for managing virus diseases in the country. Literature and nucleotide sequence database searches revealed that common bean diseases are inadequately studied and that their causal viruses have not been adequately characterized at the molecular level in Tanzania. Increased awareness on common bean virus diseases in Tanzania is expected to result into informed development of strategies for management of the same and thus increased production, which in turn has implication on nutrition and income.

Goodbye genome paper, hello genome report: the increasing popularity of 'genome announcements' and their impact on science

Next-generation sequencing technologies have revolutionized genomics and altered the scientific publication landscape. Life-science journals abound with genome papers-peer-reviewed descriptions of newly sequenced chromosomes. Although they once filled the pages of Nature and Science, genome papers are now mostly relegated to journals with low-impact factors. Some have forecast the death of the genome paper and argued that they are using up valuable resources and not advancing science. However, the publication rate of genome papers is on the rise. This increase is largely because some journals have created a new category of manuscript called genome reports, which are short, fast-tracked papers describing a chromosome sequence(s), its GenBank accession number and little else. In 2015, for example, more than 2000 genome reports were published, and 2016 is poised to bring even more. Here, I highlight the growing popularity of genome reports and discuss their merits, drawbacks and impact on science and the academic publication infrastructure. Genome reports can be excellent assets for the research community, but they are also being used as quick and easy routes to a publication, and in some instances they are not peer reviewed. One of the best arguments for genome reports is that they are a citable, user-generated genomic resource providing essential methodological and biological information, which may not be present in the sequence database. But they are expensive and time-consuming avenues for achieving such a goal.

Protists and the Wild, Wild West of Gene Expression: New Frontiers, Lawlessness, and Misfits

The DNA double helix has been called one of life's most elegant structures, largely because of its universality, simplicity, and symmetry. The expression of information encoded within DNA, however, can be far from simple or symmetric and is sometimes surprisingly variable, convoluted, and wantonly inefficient. Although exceptions to the rules exist in certain model systems, the true extent to which life has stretched the limits of gene expression is made clear by nonmodel systems, particularly protists (microbial eukaryotes). The nuclear and organelle genomes of protists are subject to the most tangled forms of gene expression yet identified. The complicated and extravagant picture of the underlying genetics of eukaryotic microbial life changes how we think about the flow of genetic information and the evolutionary processes shaping it. Here, we discuss the origins, diversity, and growing interest in noncanonical protist gene expression and its relationship to genomic architecture.

The past, present and future of mitochondrial genomics: have we sequenced enough mtDNAs?

The year 2014 saw more than a thousand new mitochondrial genome sequences deposited in GenBank—an almost 15% increase from the previous year. Hundreds of peer-reviewed articles accompanied these genomes, making mitochondrial DNAs (mtDNAs) the most sequenced and reported type of eukaryotic chromosome. These mtDNA data have advanced a wide range of scientific fields, from forensics to anthropology to medicine to molecular evolution. But for many biological lineages, mtDNAs are so well sampled that newly published genomes are arguably no longer contributing significantly to the progression of science, and in some cases they are tying up valuable resources, particularly journal editors and referees. Is it time to acknowledge that as a research community we have published enough mitochondrial genome papers? Here, I address this question, exploring the history, milestones and impacts of mitochondrial genomics, the benefits and drawbacks of continuing to publish mtDNAs at a high rate and what the future may hold for such an important and popular genetic marker. I highlight groups for which mtDNAs are still poorly sampled, thus meriting further investigation, and recommend that more energy be spent characterizing aspects of mitochondrial genomes apart from the DNA sequence, such as their chromosomal and transcriptional architectures. Ultimately, one should be mindful before writing a mitochondrial genome paper. Consider perhaps sending the sequence directly to GenBank instead, and be sure to annotate it correctly before submission.