PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.323618v1?rss=1

Authors: Egert, J., Warscheid, B., Kreutz, C.

Abstract:

Motivation: Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, the performance of different imputation methods is difficult to assess and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of a suitable imputation algorithm (DIMA). Results: The performance and broad applicability of DIMA is demonstrated on 121 quantitative proteomics data sets from the PRIDE database and on simulated data consisting of 5-50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm which is always among the three best algorithms and results in a root mean square error difference ({Delta}RMSE) <10% in 84% of the cases. Availability and Implementation: Source code is freely available for download at https://github.com/clemenskreutz/OmicsData .

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.337501v1?rss=1

Authors: Ludl, A.-A., Michoel, T.

Abstract:

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1,012 segregants from a cross between two budding yeast strains, and the YEASTRACT database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation decreases at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, where instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of systematic methods to resolve genomic linkage and pleiotropic effects from transcriptional hotspots.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.338046v1?rss=1

Authors: Hou, Q., Pucci, F., Ancien, F., Kwasigroch, J. M., Bourgeas, R., Rooman, M.

Abstract:

Motivation: Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. Results: We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale comparison of the different types of strengths and weaknesses showed their complementarity and the enhancement of the information they provide. We applied SWOTein to apocytochrome b562 and found good agreement between predicted strengths and weaknesses and native hydrogen exchange data. Its application to an amino acid-binding protein identified the hinge at the basis of the conformational change. SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. Availability: The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.339689v1?rss=1

Authors: Abitogun, F. M., Srivastava, R., Sharma, S., Komarysta, V., Akurut, E., Munir, N., Macalalad, L., Ojo-Rowland, O., Owolabi, O., Giwa, A., Debnath, S.

Abstract:

The COVID19 pandemic has resulted in 1,092,342 deaths as of 14th October 2020, indicating the urgent need for a vaccine. This study highlights novel protein sequences generated by shot gun sequencing protocols that could serve as potential antigens in the development of novel subunit vaccines and through a stringent inclusion criterion, we characterized these protein sequences and predicted their 3D structures. We found distinctly antigenic sequences from the SARS-CoV-2 that have led to identification of 4 proteins that demonstrate an advantageous binding with Human leukocyte antigen-1 molecules. Results show how previously unexplored proteins may serve as better candidates for subunit vaccine development due to their high stability and immunogenicity, reinforce by their HLA-1 binding propensities and low global binding energies. This study thus takes a unique approach towards furthering the development of vaccines by employing multiple consensus strategies involved in immuno-informatics technique.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.339499v1?rss=1

Authors: Kuang, D., Weile, J., Kishore, N., Rubin, A. F., Fields, S., Fowler, D. M., Roth, F. P.

Abstract:

Multiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating 'variant effect maps', which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets, and enable tracking and sharing of progress on ongoing MAVE projects.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.338517v1?rss=1

Authors: Guerrero-Araya, E., Munoz, M., Rodriguez, C., Paredes-Sabja, D.

Abstract:

Multilocus Sequence Typing (MLST) is a precise microbial typing approach at the intra-species level for epidemiological and evolutionary purposes. It operates by assigning a sequence type (ST) identifier to each specimen, based on a combination of allelic sequences obtained for multiple housekeeping genes included in a defined scheme. The use of MLST has multiplied due to the availability of large numbers of genomic sequences and epidemiological data in public repositories. However, data processing speed has become problematic due to datasets massive size. Here, we present FastMLST, a tool that is designed to perform PubMLST searches using BLASTn and a divide-and-conquer approach. Compared to mlst, CGE/MLST, MLSTar, and PubMLST, FastMLST takes advantage of current multi-core computers to simultaneously type thousands of genome assemblies in minutes, reducing processing times by at least 16-fold and with more than 99.95% consistency.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.336479v1?rss=1

Authors: Pavlovikj, N., Gomes-Neto, J. C., Deogun, J. S., Benson, A. K.

Abstract:

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research to diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of a given microbial species poses a tremendous opportunity for discovery and hypothesis-generating research, but such opportunity is limited by scalability and user-friendliness of existing pipelines for population-scale inquiry. Here, we present ProkEvo, an automated, scalable, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: 1) Automating and scaling the computational analysis of many thousands of bacterial genomes starting from raw Illumina paired-ended reads; 2) Using workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; 3) Utilizing high-performance and high-throughput computational platforms; 4) Generating population-based genotypic analysis at different levels of resolution using the core-genome as an input, and allelic-based or Bayesian statistical tools as classification methods; and 5) Detecting antimicrobial resistance (AMR) genes using varying databases, putative virulence factors, plasmids, and producing pan-genome annotations and data compilation that can be further utilized for analysis. The scalability of ProkEvo is shown by using two datasets with significantly different genome sizes - one with ~2,400 genomes, and the second one an order of magnitude larger containing ~23,000 genomes. Because of its modularity, the running time of ProkEvo varied from ~3-26 days depending on the dataset and the computational platform used. However, if all ProkEvo steps were ran sequentially, the running time would have varied from ~3 months to 13 years. While the running time depends on multiple factors, there is a significant advantage of using such scalable, parallelizable, and automated pipeline. ProkEvo can be used with virtually any bacterial species and the Pegasus WMS enables easy addition or removal of programs from the workflow or modification of options within them. To show this, we used ProkEvo with three important serovars of the foodborne pathogen Salmonella enterica, as well as Campylobacter jejuni and Staphylococcus aureus. These three pathogens all used different MLST scheme, and the program SISTR, which among many functions does cgMLST calls, was only applied to the S. enterica serovars. All the dependencies of ProkEvo can be distributed via conda environment or Docker image. To demonstrate ProkEvo's applicability, we have carried a population-based analysis along with the distribution of antimicrobial-associated resistance loci across datasets, and showed how to combine phylogenies with metadata using reproducible Python and R scripts. Collectively, our study shows that ProkEvo presents a viable option for scaling and automating analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.337162v1?rss=1

Authors: Bridel, S. M., Watts, S. C., Judd, L. M., Harshegyi, T., Passet, V., Rodrigues, C., Holt, K. E., Brisse, S.

Abstract:

Motivation: Klebsiella species are increasingly multidrug resistant pathogens affecting human and animal health and are widely distributed in the environment. Among these, the Klebsiella pneumoniae species complex (KpSC), which includes seven phylogroups, is an important cause of community and hospital infections. In addition, the Klebsiella oxytoca species complex (KoSC) also causes hospital infections and antibiotic-associated haemorrhagic colitis. The unsuitability of widely used clinical microbiology methods to distinguish species within each of these species complexes leads to high rates of misidentifications that are masking the true clinical significance and potential epidemiological specificities of individual species. Results: We developed a web-based tool, Klebsiella MALDI TypeR, a platform-independent and user-friendly application that enables uploading raw data from MALDI-TOF mass spectrometer to identify Klebsiella isolates at the species complex and phylogroup levels. The tool is based on a database of previously identified biomarkers that are specific for either the species complex, individual phylogroups, or related phylogroups, and is available at https://maldityper.pasteur.fr.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.07.330621v1?rss=1

Authors: Zapletal, A., Hoehler, D., Sinz, C., Stamatakis, A.

Abstract:

Scientific software from all areas of scientific research is pivotal to obtaining novel insights. Yet the quality of scientific software is rarely assessed, even though it might lead to incorrect scientific results in the worst case. Therefore, we have developed an open source tool and benchmark called SoftWipe, that pro- vides a relative software quality ranking of 51 computational tools from diverse research areas. SoftWipe can be used in the review process of software papers and to inform the scientific software selection process.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.12.336099v1?rss=1

Authors: Rahman, M. M., Kader, S. B., Rizvi, S. M. S.

Abstract:

In a try to understand the pathogenesis, evolution, and epidemiology of the SARS-CoV-2 virus, scientists from all over the world are tracking its genomic changes in real-time. Genomic studies can be helpful in understanding the disease dynamics. We have downloaded 324 complete and near-complete SARS-CoV-2 genomes submitted in the GISAID database from Bangladesh which were isolated between 30 March to 7 September 2020. We then compared these genomes with the Wuhan reference sequence and found 4160 mutation events including 2253 missense single nucleotide variations, 38 deletions, and 10 insertions. The C>T nucleotide change was most prevalent possibly due to selective mutation pressure to reduce CpG sites to evade CpG targeted host immune response. The most frequent mutation that occurred in 98% of the isolates was 3037C>T which is a synonymous change that almost always accompanied 3 other mutations that include 241C>T, 14408C>T (P323L in RdRp), and 23403A>G (D614G in spike protein). The P323L was reported to increase mutation rate and D614G is associated with increased viral replication and currently the most prevalent variant circulating all over the world. We identified multiple missense mutations in B-cell and T-cell predicted epitope regions and/or PCR target regions (including R203K and G204R that occurred in 86% of the isolates) that may impact immunogenicity and/or RT-PCR based diagnosis. Our analysis revealed 5 large deletion events in ORF7a and ORF8 gene products that may be associated with less severity of the disease and increased viral clearance. Our phylogeny analysis identified most of the isolates belonged to the Nextstrain clade 20B (86%) and GISAID clade GR (88%). Most of our isolates shared common ancestors either directly with European countries or jointly with middle eastern countries as well as Australia and India. Interestingly, the 19B clade (GISAID S clade) was unique to Chittagong which was originally prevalent in China. This reveals possible multiple introductions of the virus in Bangladesh via different routes. Hence more genome sequencing and analysis with related clinical data are needed to interpret the functional significance and better predict the disease dynamics that may be helpful for policymakers to control the COVID-19 pandemic in Bangladesh.

Copy rights belong to original authors. Visit the link for more info