KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals

BACKGROUND

Our view of host-associated microbiota remains incomplete due to the presence of as yet uncultured constituents. The Bacteroidales family S24-7 is a prominent example of one of these groups. Marker gene surveys indicate that members of this family are highly localized to the gastrointestinal tracts of homeothermic animals and are increasingly being recognized as a numerically predominant member of the gut microbiota; however, little is known about the nature of their interactions with the host.

RESULTS

Here, we provide the first whole genome exploration of this family, for which we propose the name "Candidatus Homeothermaceae," using 30 population genomes extracted from fecal samples of four different animal hosts: human, mouse, koala, and guinea pig. We infer the core metabolism of "Ca. Homeothermaceae" to be that of fermentative or nanaerobic bacteria, resembling that of related Bacteroidales families. In addition, we describe three trophic guilds within the family, plant glycan (hemicellulose and pectin), host glycan, and α-glucan, each broadly defined by increased abundance of enzymes involved in the degradation of particular carbohydrates.

CONCLUSIONS

"Ca. Homeothermaceae" representatives constitute a substantial component of the murine gut microbiota, as well as being present within the human gut, and this study provides important first insights into the nature of their residency. The presence of trophic guilds within the family indicates the potential for niche partitioning and specific roles for each guild in gut health and dysbiosis.

Structural and kinetic analyses of the protease from an amprenavir-resistant human immunodeficiency virus type 1 mutant rendered resistant to saquinavir and resensitized to amprenavir

Recent drug regimens have had much success in the treatment of human immunodeficiency virus (HIV)-infected individuals; however, the incidence of resistance to such drugs has become a problem that is likely to increase in importance with long-term therapy of this chronic illness. An analysis and understanding of the molecular interactions between the drug(s) and the mutated viral target(s) is crucial for further progress in the field of AIDS therapy. The protease inhibitor amprenavir (APV) generates a signature set of HIV type 1 (HIV-1) protease mutations associated with in vitro resistance (M46I/L, I47V, and I50V [triple mutant]). Passage of the triple-mutant APV-resistant HIV-1 strain in MT4 cells, in the presence of increasing concentrations of saquinavir (SQV), gave rise to a new variant containing M46I, G48V, I50V, and I84L mutations in the protease and a resulting phenotype that was resistant to SQV and, unexpectedly, resensitized to APV. This phenotype was consistent with a subsequent kinetic analysis of the mutant protease, together with X-ray crystallographic analysis and computational modeling which elucidated the structural basis of these observations. The switch in protease inhibitor sensitivities resulted from (i) the I50V mutation, which reduced the area of contact with APV and SQV; (ii) the compensating I84L mutation, which improved hydrophobic packing with APV; and (iii) the G-to-V mutation at residue 48, which introduced steric repulsion with the P3 group of SQV. This analysis establishes the fine detail necessary for understanding the loss of protease binding for SQV in the quadruple mutant and gain in binding for APV, demonstrating the powerful combination of virology, molecular biology, enzymology, and protein structural and modeling studies in the elucidation and understanding of viral drug resistance.

JESAM: CORBA software components to create and publish EST alignments and clusters

MOTIVATION

Expressed Sequence Tags (ESTs) are cheap, easy and quick to obtain relative to full genomic sequencing and currently sample more eukaryotic genes than any other data source. They are particularly useful for developing Sequence Tag Sites (STSs for mapping), polymorphism discovery, disease gene hunting, mass spectrometer proteomics, and most ironically for finding genes and predicting gene structure after the great effort of genomic sequencing. However, ESTs have many problems and the public EST databases contain all the errors and high redundancy intrinsic to the submitted data so it is often found that derived database views, which reduce both errors and redundancy, are more effective starting points for research than the original raw submissions. Existing derived views such as EST cluster databases and consensus databases have never published supporting evidence or intermediary results leading to difficulties trusting, correcting, and customizing the final published database. These difficulties have lead many groups to wastefully repeat the complex intermediary work of others in order to offer slightly different final views. A better approach might be to discover the most expensive common calculations used by all the approaches and then publish all intermediary results. Given a globally accessible database with a suitable component interface, like the JESAM software described in this paper, the creation of customized EST-derived databases could be achieved with minimum effort.

RESULTS

Databases of EST and full-length mRNA sequences for four model organisms have been self-compared by searching for overlaps consistent with contiguity. The sequence comparisons are performed in parallel using a PVM process farm and previous results are stored to allow incremental updates with minimal effort. The overlap databases have been published with CORBA interfaces to enable flexible global access as demonstrated by example Java applet browsers. Simple cDNA supercluster databases built as alignment database clients are themselves published via CORBA interfaces browsable with prototypical applets. A comparison with UniGene Mouse and Rat databases revealed undesirable features in both and the advantages of contrasting perspectives on complex data.

AVAILABILITY

The software is packaged as two Jar files available from: URL: http://corba.ebi.ac.uk/EST/jesam/jesam. html. One jar contains all the Java source code, and the other contains all the C, C++ and IDL code. Links to working examples of the alignment and cluster viewers (if remote firewall permits) can be found at http://corba.ebi.ac.uk/EST. All the Washington University mouse EST traces are available for browsing at the same URL.

DNA sequence chromatogram browsing using JAVA and CORBA

DNA sequence chromatograms (traces) are the primary data source for all large-scale genomic and expressed sequence tags (ESTs) sequencing projects. Access to the sequencing trace assists many later analyses, for example contig assembly and polymorphism detection, but obtaining and using traces is problematic. Traces are not collected and published centrally, they are much larger than the base calls derived from them, and viewing them requires the interactivity of a local graphical client with local data. To provide efficient global access to DNA traces, we developed a client/server system based on flexible Java components integrated into other applications including an applet for use in a WWW browser and a stand-alone trace viewer. Client/server interaction is facilitated by CORBA middleware which provides a well-defined interface, a naming service, and location independence. [The software is packaged as a Jar file available from the following URL: http://www.ebi.ac.uk/jparsons. Links to working examples of the trace viewers can be found at http://corba.ebi.ac.uk/EST. All the Washington University mouse EST traces are available for browsing at the same URL.]

DNA Sequence Chromatogram Browsing Using JAVA and CORBA

DNA sequence chromatograms (traces) are the primary data source for all large-scale genomic and expressed sequence tags (ESTs) sequencing projects. Access to the sequencing trace assists many later analyses, for example contig assembly and polymorphism detection, but obtaining and using traces is problematic. Traces are not collected and published centrally, they are much larger than the base calls derived from them, and viewing them requires the interactivity of a local graphical client with local data. To provide efficient global access to DNA traces, we developed a client/server system based on flexible Java components integrated into other applications including an applet for use in a WWW browser and a stand-alone trace viewer. Client/server interaction is facilitated by CORBA middleware which provides a well-defined interface, a naming service, and location independence.

[The software is packaged as a Jar file available from the following URL: http://www.ebi.ac.uk/∼jparsons. Links to working examples of the trace viewers can be found athttp://corba.ebi.ac.uk/EST. All the Washington University mouse EST traces are available for browsing at the same URL.]

Lane tracking software for four-color fluorescence-based electrophoretic gel images

Software to track sample lanes automatically in four-color, fluorescence-based, electrophoretic gel images has been developed for application in large-scale DNA sequencing projects. Lanes and lane boundaries are tracked by analyzing a first difference approximation to the gradient of a vertically integrated and processed "brightness" profile. Initially lanes are located in a region of the gel image selected for good horizontal lane spacing and signal strength. The software uses models of expected lane and interlane spacing and lateral lane behavior to maintain accurate tracking on imperfect gels. In areas where intensity-based tracking is difficult, interpixel column correlation is also used to locate and define lane features. Summary statistics and compressed-in-time images are generated for user evaluation of tracking performance. The software developed has been tested successfully on gel images with degradations including significant horizontal lane motion (curving) and image artifacts, and is now in full-scale use in our sequencing projects.

Miropeats: graphical DNA sequence comparisons

Miropeats displays DNA sequence similarity information graphically. The program discovers regions of similarity amongst any set of DNA sequences and then draws a graphic that summarizes the length, location and relative orientations of any repeated sequences. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing large-scale sequence similarities on a single page of PostScript graphics. Miropeats has been applied estensively to help understand shotgun assembly projects, to check cosmid overlaps and to perform inter-genomic comparisons.

Improved tools for DNA comparison and clustering

DNA sequence clustering is an effective aid of the comprehension, summarization and compression of DNA sequence databases. Previous work created programs suitable for the comparison and clustering of cDNA sequences but new enhanced programs have been written to cluster genomic DNA fragments, large EST projects, and entire DNA databases. Three new programs (ICAtools) are discussed: ICAass, N2tool, and ICAmatches. ICAass has been used to compress the EMBL database by hiding or removing sequences with various degrees of redundancy. It also has the fastest database querying mode. N2tool provides fast and sensitive clustering of genomic fragment databases on the basis of small areas of local similarity. N2tool has proven utility in the discovery of contaminating vector or other artefactual sequence when the potential contaminant is not otherwise known. ICAmatches is a new cluster analysis program that uses a novel alignment style to present multiple alignment summaries. All the tools are convenient to use because they share a common memory-frugal index format and accept most DNA sequence formats directly.

Clustering cDNA sequences

A set of programs has been written to quantify the similarities between large numbers of cDNA sequences. This information is used to cluster similar sequences together. The main program can cluster thousands of cDNA sequences per day using a novel, computationally inexpensive algorithm. The clustering information is kept in a small index file so that disk storage requirements are negligible. Using this index file, subsidiary programs create various views and statistical summaries of the entire cDNA sequence collection.