PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.372003v1?rss=1

Authors: Gado, J., Harrison, B. E., Sandgren, M., Stahlberg, J., Beckham, G. T., Payne, C. M.

Abstract:

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These important enzymes are often bimodular, comprised of a catalytic domain attached to a carbohydrate binding module (CBM) via a flexible linker, and exhibit a long active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical biological and industrial importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, using the number of residues in the active-site loops as features, were able discriminate GH7 CBHs and EGs with up to 99% accuracy. The lengths of the A4, B2, B3, and B4 loops were strongly correlated with functional subtype across the GH7 family. Position-specific classification rules were derived such that specific amino acids at 42 different sequence positions predicted the functional subtype with accuracies greater than 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. We propose these positions play vital roles in the functional variation of GH7 cellulases. Taken together, our results complement numerous experimental findings and present functional relationships that can be applied when prospecting GH7 cellulases from nature, for sequence annotation, and to understand or manipulate function.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371724v1?rss=1

Authors: Overbey, E. G., Saravia-Butler, A. M., Zhang, Z., Rathi, K. S., Fogle, H., da Silveira, W. A., Barker, R. J., Bass, J. J., Beheshti, A., Berrios, D. C., Blaber, E. A., Cekanaviciute, E., Costa, H. A., Davin, L. B., Fisch, K. M., Gebre, S. G., Geniza, M., Gilbert, R., Gilroy, S., Hardiman, G., Herranz, R., Kidane, Y. H., Kruse, C. P. S., Lee, M. D., Liefeld, T., Lewis, N. G., McDonald, J. T., Meller, R., Mishra, T., Perera, I., Ray, S., Reinsch, S., Rosenthal, S. B., Strong, M., Szewczyk, N. J., Tahimic, C. G. T., Taylor, D. M., Vandenbrink, J. P., Villacampa, A., Weging, S., Wolverton, C., Wya

Abstract:

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.375758v1?rss=1

Authors: Crow, M., Suresh, H., Lee, J., Gillis, J.

Abstract:

What makes a mouse a mouse, and not a hamster? The answer lies in the genome, and more specifically, in differences in gene regulation between the two organisms: where and when each gene is expressed. To quantify differences, a typical study will either compare functional genomics data from homologous tissues, limiting the approach to closely related species; or compare gene repertoires, limiting the resolution of the analysis to gross correlations between phenotypes and gene family size. As an alternative, gene coexpression networks provide a basis for studying the evolution of gene regulation without these constraints. By incorporating data from hundreds of independent experiments, meta-analytic coexpression networks reflect the convergent output of species-specific transcriptional regulation. In this work, we develop a measure of regulatory evolution based on gene coexpression. Comparing data from 14 species, we quantify the conservation of coexpression patterns 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. Strikingly, we uncover deeply conserved patterns of gradient-like expression across cell types from both the animal and plant kingdoms. These results suggest that ancient genes contribute to transcriptional cell identity through mechanisms that are independent of duplication and divergence.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.330183v1?rss=1

Authors: He, S., Schein, A., Sarsani, V., Flaherty, P.

Abstract:

There are distinguishing features or "hallmarks" of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.09.373795v1?rss=1

Authors: Danesh, G., Saulnier, E., Gascuel, O., Choisy, M., Alizon, S.

Abstract:

We introduce TiPS, an R-based simulation software to generate time series and genealogies associated with a population dynamics model. The approach is flexible since it can capture any model defined with a set of ordinary differential equations (ODE), and allow parameter values to vary over time periods. Computational time is minimal thanks to the use of the Rcpp package to compile the ODEs into a program corresponding to an implementation of the Gillespie algorithm. This software is particularly suited for epidemiology and phylodynamics, where there is a need to generate numerous phylogenies for a variety of infections life cycles, and in population genetics as well.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.370601v1?rss=1

Authors: Gilchrist, C. L. M., Booth, T. J., Chooi, Y.-H. H.

Abstract:

Genes involved in coordinated biological pathways, including metabolism, drug resistance and virulence, are often collocalised as gene clusters. Identifying homologous gene clusters aids in the study of their function and evolution, however existing tools are limited to searching local sequence databases. Tools for remotely searching public databases are necessary to keep pace with the rapid growth of online genomic data. Here, we present cblaster, a Python based tool to rapidly detect collocated genes in local and remote databases. cblaster is easy to use, offering both a command line and a user-friendly graphical user interface (GUI). It generates outputs that enable intuitive visualisations of large datasets, and can be readily incorporated into larger bioinformatic pipelines. cblaster is a significant update to the comparative genomics toolbox. cblaster source code and documentation is freely available from GitHub under the MIT license (https://www.github.com/gamcil/cblaster).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.373720v1?rss=1

Authors: Xu, D., Lu, Z., Jin, K., Qiu, W., HanQiao, G., Han, X., Zhuo, R.

Abstract:

Efficiently extracting information from biological big data can be a huge challenge for people (especially those who lack programming skills). We developed Sequence Processing and Data Extraction (SPDE) as an integrated tool for sequence processing and data extraction for gene family and omics analyses. Currently, SPDE has seven modules comprising 100 basic functions that range from single gene processing (e.g., translation, reverse complement, and primer design) to genome information extraction. All SPDE functions can be used without the need for programming or command lines. The SPDE interface has enough prompt information to help users run SPDE without barriers. In addition to its own functions, SPDE also incorporates the publicly available analyses tools (such as, NCBI-blast, HMMER, Primer3 and SAMtools), thereby making SPDE a comprehensive bioinformatics platform for big biological data analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371849v1?rss=1

Authors: Wang, X., Xu, Z., Zhou, X., Zhang, Y., Huang, H., Ding, Y., Duerr, R. H., Chen, W.

Abstract:

The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have quickly gained enormous popularity, novel methods for analyzing this new type of single cell multi-omics data are still in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT can be used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include 1) using confident cell type labels identified from surface protein data as guidance for cell clustering, 2) providing general annotation of confident cell types for each cell cluster, 3) fully utilizing cells with uncertain or missing cell type labels to increase performance, and 4) accurate prediction of confident cell types identified from surface protein data for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results, and our framework can be easily extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house real datasets. We believe this new method will greatly help researchers characterize novel cell types and make new biological discoveries using single cell multi-omics data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.07.372672v1?rss=1

Authors: Ay, D., Tastan, O.

Abstract:

The treatment decisions for a cancer patient are typically based on the patient's diagnosed cancer type. With the characterization of cancer tumors at the molecular level, there have been reports of patients that bear molecular similarities to other patients that are diagnosed with other cancer types. Motivated from these observations, we aim at discovering cross-cancer patients, which we define as patients whose tumors are more similar to patient tumors diagnosed with another cancer type. Our framework, DeepCrossCancer, identifies a core set of cross-cancer patients that always co-cluster with the other patient from another cancer type. The input to DeepCrossCancer is the transcriptomic profiles of the patient tumors, the age, and gender of the patient. To solve the clustering problem, we propose a deep learning-based clustering method in which the clustering task is supervised by cancer type labels and the survival times of the patients. Applying the method to patient data from nine different cancers, we discover 20 cross-cancer patients. By analyzing the predictive genes of the cross-cancer patients and other genomic information available for the patient such as somatic mutations and copy number variations, we identify striking similarities across these patients validating their similarities. The detection of cross-cancer patients opens up possibilities for transferring clinical decisions across patients at a single patient level. DeepCrossCancer is available at https://github.com/Tastanlab/DeepCrossCancer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.370650v1?rss=1

Authors: Gilchrist, C. L. M., Chooi, Y.-H. H.

Abstract:

Genes involved in biological pathways are often collocalised in gene clusters, the comparison of which can give valuable insights into their function and evolutionary history. However, comparison and visualisation of gene cluster homology is a tedious process, particularly when many clusters are being compared. Here, we present clinker, a Python based tool, and clustermap.js, a companion JavaScript visualisation library, which used together can automatically generate accurate, interactive, publication-quality gene cluster comparison figures directly from sequence files. Source code and documentation for clinker and clustermap.js is available on GitHub (github.com/gamcil/clinker and github.com/gamcil/clustermap.js, respectively) under the MIT license. clinker can be installed directly from the Python Package Index via pip.

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