PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.04.325670v1?rss=1

Authors: Li, R., Qu, H., Wang, S., Wang, X., Cui, Y., Yu, L., Chater, J. M., Zhou, R., Jia, Q., Traband, R., Yuan, D., Zhu, J., Zhong, W.-D., Jia, Z.

Abstract:

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising biomarkers for a variety of human diseases, including cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive diagnostic biomarkers for early cancer detection. The vast amount of miRNA expression data from tens of thousands of samples in various types of cancers generated by The Cancer Genome Atlas (TCGA) and circulating miRNA data produced by many large-scale circulating miRNA profiling studies provide extraordinary opportunities for the discovery and validation of miRNA signatures in cancer. Novel and user-friendly tools are desperately needed to facilitate the data mining of such valuable cancer miRNome datasets. To fill this void, we developed CancerMIRNome, a web server for interactive analysis and visualization of cancer miRNome data based on TCGA and public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and functions have been packaged in CancerMIRNome, allowing for a pan-cancer analysis of a miRNA of interest across multiple cancer types and a comprehensive analysis of cancer miRNome at the dataset level. The CancerMIRNome web server is freely available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.05.326504v1?rss=1

Authors: Robeson, M. S., O'Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., Bokulich, N. A.

Abstract:

Background: Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardizations limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a software package for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. Results: To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA, and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. Conclusions: RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.04.325571v1?rss=1

Authors: McLay, T. G., Gunn, B. F., Ning, W., Tate, J. A., Nauheimer, L., Joyce, E. M., Simpson, L., Schmidt-Lebuhn, A. N., Baker, W. J., Forest, F., Jackson, C. J.

Abstract:

Universal target enrichment kits maximise utility across wide evolutionary breadth while minimising the number of baits required to create a cost-efficient kit. Locus assembly requires a target reference, but the taxonomic breadth of the kit means that target references files can be phylogenetically sparse. The Angiosperms353 kit has been successfully used to capture loci throughout angiosperms but includes sequence information from 6-18 taxa per locus. Consequently, reads sequenced from on-target DNA molecules may fail to map to references, resulting in fewer on-target reads for assembly, reducing locus recovery. We expanded the Angiosperms353 target file, incorporating sequences from 566 transcriptomes to produce a mega353 target file, with each gene represented by 17-373 taxa. This mega353 file is a drop-in replacement for the original Angiosperms353 file in HybPiper analyses. We provide tools to subsample the file based on user-selected taxon groups, and to incorporate other transcriptome or protein-coding gene datasets. Compared to the default Angiosperms353 file, the mega353 file increased the percentage of on-target reads by an average of 31%, increased loci recovery at 75% length by 61.9%, and increased the total length of the concatenated loci by 30%. The mega353 file and associated scripts are available at: https://github.com/chrisjackson-pellicle/NewTargets

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.04.325274v1?rss=1

Authors: Ronen, O., Zuk, O.

Abstract:

Prediction of Proteins' three-dimensional structure and their contact maps from their amino-acid sequences is a fundamental problem in structural computational biology. The structure and contacts shed light on protein function, enhance our basic understanding of their molecular biology and may potentially aid in drug design. In recent years we have seen significant progress in protein contact map prediction from Multiple Sequence Alignments (MSA) of the target protein and its homologous, using signals of co-evolution and applying deep learning methods. Homology modelling is a popular and successful approach, where the structure of a protein is determined using information from known template structures of similar proteins, and has been shown to improve prediction even in cases of low sequence identity. Motivated by these observations, we developed Periscope, a method for homology-assisted contact map prediction using a deep convolutional network. Our method automatically integrates the co-evolutionary information from the MSA, and the physical contact information from the template structures. We apply our method to families of CAMEO and membrane proteins, and show improved prediction accuracy compared to the MSA-only based method RaptorX. Finally, we use our method to improve the subsequent task of predicting the proteins' three-dimensional structure based on the (improved) predicted contact map, and show initial promising results in this task too - our overall accuracy is comparable to the template-based Modeller software, yet the two methods are complementary and succeed on different targets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.03.324665v1?rss=1

Authors: Chen, C., Zhan, L., Pan, X., Wang, Z., Guo, X., Qin, H., Xiong, F., Shi, W., Shi, M., Ji, F., Wang, Q., Yu, N., Xiao, R.

Abstract:

Background: Auditory brainstem response (ABR) test is widely used in newborn hearing screening and hearing disease diagnosis. Identifying and marking are challenging and repetitive tasks because of complex rules of ABR characteristic waveform and interference of background noise. Methods: This study proposes an automatic method to recognize ABR characteristic waveform. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding. Results: Specific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data, and recognition accuracy of waves I, III, and V can reach 92.91%. Discussion: Thus, the proposed method can reduce the repetitive work of doctors and meet accuracy effectively. Therefore, this method has clinical potential.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320259v1?rss=1

Authors: Kühlwein, S. D., Ikonomi, N., Schwab, J. D., Kraus, J. M., Rudolph, K. L., Pfister, A., Schuler, R., Kühl, M., Kestler, H. A.

Abstract:

Biological processes are rarely a consequence of single protein interactions but rather of complex regulatory networks. However, interaction graphs cannot adequately capture temporal changes. Among models that investigate dynamics, Boolean network models can approximate simple features of interaction graphs integrating also dynamics. Nevertheless, dynamic analyses are time-consuming and with growing number of nodes may become infeasible. Therefore, we set up a method to identify minimal sets of nodes able to determine network dynamics. This approach is able to depict dynamics without calculating exhaustively the complete network dynamics. Applying it to a variety of biological networks, we identified small sets of nodes sufficient to determine the dynamic behavior of the whole system. Further characterization of these sets showed that the majority of dynamic decision-makers were not static hubs. Our work suggests a paradigm shift unraveling a new class of nodes different from static hubs and able to determine network dynamics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.02.324616v1?rss=1

Authors: Taguchi, Y.-h., Turki, T.

Abstract:

Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted $P$-values of 0.1 when the null distribution is generated by gene order shuffling. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.02.324319v1?rss=1

Authors: Pirayre, A., Duval, L., Blugeon, C., Firmo, C., Perrin, S., Jourdier, E., Margeot, A., Bidard-Michelot, F.

Abstract:

Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network. Results: Experimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network (GRN) built using the BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the {beta}-glucosidase regulation andiii)$ a negative regulation of the development process and growth. Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the {beta}-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.04.325233v1?rss=1

Authors: Zelig, A., Kaplan, N.

Abstract:

The challenges of clustering noisy high-dimensional biological data have spawned advanced clustering algorithms that are tailored for specific subtypes of biological datatypes. However, the performance of such methods varies greatly between datasets, they require post hoc tuning of cryptic hyperparameters, and they are often not transferable to other types of data. Here we present a novel generic clustering approach called k minimal distances (KMD) clustering, based on a simple generalization of single and average linkage hierarchical clustering. We show how a generalized silhouette-like function is predictive of clustering accuracy and exploit this property to eliminate the main hyperparameter k. We evaluated KMD clustering on standard simulated datasets, simulated datasets with high noise added, mass cytometry datasets and scRNA-seq datasets. When compared to standard generic and state-of-the-art specialized algorithms, KMD clustering's performance was consistently better or comparable to that of the best algorithm on each of the tested datasets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.322362v1?rss=1

Authors: Miller, B., Morse, A., Borgert, J. E., Liu, Z., Sinclair, K., Gamble, G., Zou, F., Newman, J., Leon-Novelo, L., Marroni, F., McIntyre, L.

Abstract:

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? Tests of allelic effect are performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between non-tester alleles, allowing n alleles to be compared with n crosses. Using a mouse dataset where both testcrosses and direct comparisons have been performed, we show that ~75% of the predicted differences between non-tester alleles are validated in a background of ~10% differences in AI. The testing for AI involves several complex bioinformatics steps. BASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BASE has been packaged in Galaxy, made available in Nextflow and sbatch on github (https://github.com/McIntyre-Lab/BASE_2020). In the mouse data, the direct test identifies more cis effects than the testcross. Cis-by-trans interactions with trans-acting factors on the X contributing to observed cis effects in autosomal genes in the direct cross remains a possible explanation for the discrepancy.

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