Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis

Here, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real data. Using Kssd, we prioritize references for all 1,019,179 bacteria whole genome sequencing (WGS) runs from NCBI Sequence Read Archive and find misidentification or contamination in 6164 of these. Additionally, we analyze WGS and exome runs of samples from the 1000 Genomes Project.

Combining Natural Language Processing and Metabarcoding to Reveal Pathogen-Environment Associations

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year—with 180,000 resulting deaths—mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations.Author SummaryOur finding that C. neoformans is associated with decomposing wood is reinforced by the general literature on C. neoformans and its close congeneric relatives and warrants further investigation. This work demonstrates the potential utility of pairing Natural Language Processing (NLP) with single-locus metagenetic data for the study of Neglected Tropical Diseases. We present a novel method to study the ecological niches of rare pathogens that leverages the immense amount of data available to researchers in the NCBI Sequence Read Archive (SRA)combined with a text-mining analysis based on Natural Language Processing. We demonstrate that text processing, noun identification, and verb identification can play an important role in analyzing a large corpus of documents together with metagenetic data. Forging this connection requires access to all of the available ecological 18S ribosomal RNA and Internal Transcribed Spacer NCBI SRA datasets. These datasets use metabarcoding to query taxonomic diversity in eukaryotic organisms, and in the case of the Internal Transcribed Spacer, they specifically target Fungi. The presence of specific species is inferred when diagnostic 18S or ITS gene region sequences are found in the SRA data. We searched for C. neoformans in all 18S and ITS datasets available and gathered all associated journal articles that either cite the SRA data accessions or are cited in the SRA data accessions.Published metagenetic data often have associated metadata including: latitude and longitude, temperature, and other physical characteristics describing the conditions in which the metagenetic sample was collected. These metadata are not always be presented in consistent formats, so harmonizing study methods may be needed to appropriately compare metagenetic data as commonly required in metanalysis studies. We present an analysis which takes as input articles associated with SRA datasets that were found to contain evidence of C. neoformans. We apply NLP methods to this corpus of articles to describe the niche of C. neoformans. Our results reinforce the current understanding of C. neoformans’s niche, indicating the pertinence of employing a NLP analysis to identify the niche of an organism. This approach could further the description of virtually any other organism that routinely appears in metagenetic surveys, especially pathogens, whose ecological niches are unknown or poorly understood.Optional Striking ImageCryptococcus neoformans cells budding. Image Provided Courtesy of Felipe H. Santiago-Tirado, colored by Kristina Davis, CC-BY 4.0

pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive

The NCBI Sequence Read Archive (SRA) is the primary archive of next-generation sequencing datasets. SRA makes metadata and raw sequencing data available to the research community to encourage reproducibility and to provide avenues for testing novel hypotheses on publicly available data. However, methods to programmatically access this data are limited. We introduce the Python package, pysradb, which provides a collection of command line methods to query and download metadata and data from SRA, utilizing the curated metadata database available through the SRAdb project. We demonstrate the utility of pysradb on multiple use cases for searching and downloading SRA datasets. It is available freely at https://github.com/saketkc/pysradb.

pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive

NCBIs Sequence Read Archive (SRA) is the primary archive of next-generation sequencing datasets. SRA makes metadata and raw sequencing data available to the research community to encourage reproducibility, and to provide avenues for testing novel hypotheses on publicly available data. However, existing methods to programmatically access these data are limited. We introduce a Python packagepysradbthat provides a collection of command line methods to query and download metadata and data from SRA utilizing the curated metadata database available through the SRAdb project. We demonstrate the utility ofpysradbon multiple use cases for searching and downloading SRA datasets. It is available freely athttps://github.com/saketkc/pysradb.

A bioinformatics approach to identifyingWolbachiainfections in arthropods

Wolbachiais the most widespread endosymbiont, infecting >20% of arthropod species, and capable of drastically manipulating the host’s reproductive mechanisms. Conventionally, diagnosis has relied on PCR amplification; however, PCR is not always a reliable diagnostic technique due to primer specificity, strain diversity, degree of infection and/or tissue sampled. Here, we look for evidence ofWolbachiainfection across a wide array of arthropod species using a bioinformatic approach to detect theWolbachiagenesftsZ, wsp,and thegroEoperon in next-generation sequencing samples available through the NCBI Sequence Read Archive. For samples showing signs of infection, we attempted to assemble entireWolbachiagenomes, and in order to better understand the relationships between hosts and symbionts, phylogenies were constructed using the assembled gene sequences. Out of the 34 species with positively identified infections, eight species of arthropod had not previously been recorded to harborWolbachiainfection. All putative infections cluster with known representative strains belonging to supergroup A or B, which are known to only infect arthropods. This study presents an efficient bioinformatic approach for post-sequencing diagnosis and analysis ofWolbachiainfection in arthropods.

Relationships within Cladobranchia (Gastropoda: Nudibranchia) based on RNA-Seq data: an initial investigation

Cladobranchia (Gastropoda: Nudibranchia) is a diverse (approx. 1000 species) but understudied group of sea slug molluscs. In order to fully comprehend the diversity of nudibranchs and the evolution of character traits within Cladobranchia, a solid understanding of evolutionary relationships is necessary. To date, only two direct attempts have been made to understand the evolutionary relationships within Cladobranchia, neither of which resulted in well-supported phylogenetic hypotheses. In addition to these studies, several others have addressed some of the relationships within this clade while investigating the evolutionary history of more inclusive groups (Nudibranchia and Euthyneura). However, all of the resulting phylogenetic hypotheses contain conflicting topologies within Cladobranchia. In this study, we address some of these long-standing issues regarding the evolutionary history of Cladobranchia using RNA-Seq data (transcriptomes). We sequenced 16 transcriptomes and combined these with four transcriptomes from the NCBI Sequence Read Archive. Transcript assembly using Trinity and orthology determination using H a MS t R yielded 839 orthologous groups for analysis. These data provide a well-supported and almost fully resolved phylogenetic hypothesis for Cladobranchia. Our results support the monophyly of Cladobranchia and the sub-clade Aeolidida, but reject the monophyly of Dendronotida.