PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.373555v1?rss=1

Authors: Tabe-Bordbar, S., Song, Y. J., Lunt, B. J., Prasanth, K. V., Sinha, S.

Abstract:

Background: Estrogen Receptor (ER) is a major lineage determining transcription factor (TF) in mammary gland development, orchestrating the expression of thousands of genes. Dysregulation of ER-mediated transcriptional program results in abnormal cell proliferation and cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. Results: A subset of ER-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ER-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ER-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants. Conclusions: We modeled the sequence-to-expression relationship in ER-driven enhancers and gained mechanistic insights into the workings of a major transcriptional program. Our model is consistent with the current body of knowledge and its predictions are confirmed by experimental observations. We believe this to be a promising approach to analysis of regulatory sequences and variants.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.373050v1?rss=1

Authors: Ben-Ari, Y., Shamir, R. B., Orenstein, Y., Pu, L.

Abstract:

High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast speed and extremely low cost. As a consequence, sequencing techniques have become ubiquitous in biomedical research and are used in hundreds of genomic applications. Efficient data structures and algorithms have been developed to handle the large datasets produced by these techniques. The prevailing method to index DNA sequences in those data structures and algorithms is by k-mers (k-long substrings) known as minimizers. Minimizers are the smallest k-mers selected in every consecutive window of a fixed length in a sequence, where the smallest is determined according to a predefined order, e.g., lexicographic. Recently, a new k-mer order based on a universal hitting set (UHS) was suggested. While several studies have shown that orders based on a small UHS have improved properties, the utility of using a small UHS in high-throughput sequencing analysis tasks has not been demonstrated to date. Here, we demonstrate the practical benefit of UHSs for the first time, in the genome assembly task. Re-constructing a genome from billions of short reads is a fundamental task in high-throughput sequencing analyses. de Bruijn graph construction is a key step in genome assembly, which often requires very large amounts of memory and long computation time. A critical bottleneck lies in the partitioning of DNA sequences into bins. The sequences in each bin are assembled separately, and the final de Bruijn graph is constructed by merging the bin-specific subgraphs. We incorporated a UHS-based order in the bin partition step of the Minimum Substring Partitioning algorithm of Li et al. (2013). Using a UHS-based order instead of lexicographic- or random-ordered minimizers produced lower density minimizers with more balanced bin partitioning, which led to a reduction in both runtime and memory usage

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371542v1?rss=1

Authors: Mieth, B., Rozier, A., Rodriguez, J. A., Hohne, M. M.- C., Gornitz, N., Muller, K. R.

Abstract:

Deep learning algorithms have revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence (XAI) has emerged as a novel area of research that goes beyond pure prediction improvement. Knowledge embodied in deep learning methodologies is extracted by interpreting their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers' decisions by applying layerwise relevance propagation as one example from the pool of XAI techniques. The resulting importance scores are eventually used to determine a subset of most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 WTCCC study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw p-value thresholding as well as other baseline methods. Moreover, two novel disease associations (rs10889923 for hypertension and rs4769283 for type 1 diabetes) were identified.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.07.373043v1?rss=1

Authors: Qiu, Y., Wang, J., Lei, J., Roeder, K.

Abstract:

Motivation: Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results: To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation: We implement this method as an R package markerpen, hosted on https://github.com/yixuan/markerpen.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.08.373092v1?rss=1

Authors: Mohabati, R., Rezaei, R., Mohajel, N., Ranjbar, M. M., Azadmanesh, K., Roohvand, F.

Abstract:

Producing a functional consensus sequence is a preliminary bioinformatics task, which is a necessity for many research purposes. However, the existence of hypervariable regions in the input multiple sequence alignment files causes complications in generating a useful consensus sequence. The current methods for consensus generation, Threshold, and majority algorithms, have several problems, which exclude them as applicable algorithms for such highly variable sequence clusters. Hence, we designed a novel alternative algorithm for the same purpose. The algorithm was explained both using a mathematical formula and a practical implementation in Python programming language. A sequence set from HCV genotype 1b E2 protein has been utilized as a practical example for evaluating the performance of the algorithm. A few in silico tests have been performed on the output sequence and the results have been compared to results from other algorithms. Epitope-mapping analysis indicates the functionality of this algorithm, by preserving the hotspot residues in the consensus sequence, and the antigenicity index shows significant antigenicity of the consensus sequence. Moreover, phylogenetic analysis shows no significant change in the placement of the new consensus sequence on the phylogenetic tree compared to other algorithms. This approach will have several implications in designing a new vaccine for highly variable viruses such as HIV-1, Influenza, and Hepatitis C Viruses (HCV).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.07.372466v1?rss=1

Authors: Mahbub, S., Bayzid, M. S.

Abstract:

Motivation: Protein-protein interactions are central to most biological processes. However, reliable identification of protein-protein interaction (PPI) sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites. Results: We present EGAT, a highly accurate deep learning based method for PPI site prediction, where we have introduced a novel edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved remarkable improvement over the best alternate methods. Furthermore, EGAT offers a more interpretable framework than the typical black-box deep neural networks. Availability: EGAT is freely available as an open source project at https://github.com/Sazan-Mahbub/EGAT.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.367409v1?rss=1

Authors: Qiu, X., Feit, A., Feiglin, A., Xie, Y., Kesten, N., Taing, L., Perkins, J., Zhou, N., Gu, S., Li, Y., Cejas, P., Jeselsohn, R., Brown, M., Liu, X. S., Long, H. W.

Abstract:

ChIP-seq and ATAC-seq have become essential technologies used as effective methods of measuring protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates correct normalization between samples, adjustment of copy number variations, and integration of new downstream analysis tools. Here we present CoBRA, a modularized computational workflow which quantifies ChIP and ATAC-seq peak regions and performs unsupervised and supervised analysis. CoBRA provides a comprehensive state-of-the-art ChIP and ATAC-seq analysis pipeline that is usable by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference DB and pathway analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371195v1?rss=1

Authors: Cabrera, J. L., Enriquez, J. A., Garcia, J., Sanchez-Cabo, F.

Abstract:

Accurate pathogenic detection for single nucleotide variants (SNVs) is a key problem to perform variant ranking in whole exome sequencing studies. Several in silico tools have been developed to identify deleterious variants. Locus variability, computed as Shannon entropy from gnomAD/helixMTdb variant allele frequencies can be used as pathogenic variants predictor. In this study we evaluate the use of Shannon entropy in non-coding mitochondrial DNA and also in coding regions with an additional selective pressure other than that imposed by the genetic code, as are splice-sites. To benchmark this functionality in non-coding mitochondrial variants, Shannon entropy was compared with HmtVar disease score, outperforming it in non-coding SNVs (AUCH=0.99 in ROC curve and PR-AUCH=1.00 in Precision-recall curve). In the same way, for splice-sites' variants, Shannon entropy was compared against two state-of-the-art ensemble predictors ada score and rf score, matching their overall performance both in ROC curves (AUCH=0.95) and Precision-recall curves (PR-AUC=0.97). Therefore, locus variability could aid in variant ranking process for these specific types of SNVs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.372219v1?rss=1

Authors: Wintersinger, J. A., Dobson, S. M., Dick, J. E., Morris, Q.

Abstract:

Cancers are composed of multiple genetically distinct subpopulations of cancer cells. By performing genome sequencing on tissue samples from a cancer, we can infer the existence of these subpopulations, which mutations render them genetically unique, and the evolutionary relationships between subpopulations. This can reveal critical points in disease development and inform treatment. Here we present Pairtree, a new algorithm for constructing evolutionary trees that reveal relationships between genetically distinct cell subpopulations composing a patient's cancer. Pairtree focuses on performing these reconstructions using dozens of cancerous tissue samples per patient, which can be taken from different points in space (e.g., primary tumour and metastasis) or in time (e.g., at diagnosis and at relapse). In concert, these can reveal thirty or more distinct subpopulations, and show how their composition changed between tissue samples. Each additional tissue sample from a patient provides additional constraints on possible evolutionary histories, and so should aid construction of more accurate and precise results. Counterintuitively, we demonstrate using both simulated and real data that existing algorithms actually perform worse as additional tissue samples are provided, often failing to produce any result. Pairtree, conversely, efficiently leverages the information from additional samples to perform progressively better as samples are added. The algorithm's ability to function in these settings enables new biological and clinical applications, which we demonstrate using data from 14 acute lymphoblastic leukemia cancers, with dozens of tissue samples per cancer. Pairtree also produces a useful visual representation of the degree of support underlying evolutionary relationships present in the user's data, allowing users to make accurate inferences from its results.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.07.372524v1?rss=1

Authors: Wei, C., Zhang, J., Yuan, X., He, Z., Liu, G.

Abstract:

Protein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifier capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping kmer, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. In fact, kmer features that count the occurrence frequency of trinucleotides only reflect the local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. In viewing of the point, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploiting global sequence order information, non-overlapping kmer features and statistical dependencies among coding labels. Evaluated on genomic and transcript sequences, our proposed method significantly outperforms existing state-of-the-art methods.

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