PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.358218v1?rss=1

Authors: Shen, Y., Chen, L.-L., Gao, J.

Abstract:

Chromatin accessibility is a highly informative structural feature for understanding gene transcription regulation because it indicates the degree to which nuclear macromolecules such as proteins and RNA can access chromosomal DNA. Studies show that chromatin accessibility is highly dynamic during stress response, stimulus response, and developmental transition. Moreover, physical access to chromosomal DNA in eukaryotes is highly cell-specific. Therefore, current technologies such as DNase-seq, ATAC-seq, and FAIRE-seq reveal only a portion of the open chromatin regions (OCRs) present in a given species. Thus, the genome-wide distribution of OCRs remains unknown. In this study, we developed a bioinformatics tool called CharPlant for the de novo prediction of chromatin accessible regions in plant genomes. To develop this tool, we constructed a three-layer convolutional neural network (CNN) and subsequently trained the CNN using DNase-seq and ATAC-seq datasets of four plant species. The model simultaneously learns the sequence motifs and regulatory logics, which are jointly used to determine DNA accessibility. All of these steps are integrated into CharPlant, which can be run using a simple command line. The results of data analysis using CharPlant in this study demonstrate its prediction power and computational efficiency. To our knowledge, CharPlant is the first de novo prediction tool that can identify potential OCRs in the whole genome. The source code of CharPlant and supporting files are freely downloadable from https://github.com/Yin-Shen/CharPlant.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.357756v1?rss=1

Authors: Lin, D., Sanders, J., Noble, W. S.

Abstract:

Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to asses experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high resolution Hi-C data or on large single-cell Hi-C datasets. We introduce a Python implementation of HiCRep and demonstrate that it is much faster than the existing R implementation. Furthermore, we give examples of HiCRep's ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data. HiCRep.py and its documentation are available with a GPL license at https://github.com/Noble-Lab/hicrep. The software may be installed automatically using the pip package installer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.356717v1?rss=1

Authors: Liany, H., Jeyasekharan, A., Rajan, V.

Abstract:

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging large-scale unified gene expression data of both disease-free and cancerous data, we design a new technique, based on statistical hypothesis testing, called ASTER (Analysis of Synthetic lethality by comparison with Tissue-specific disease-free gEnomic and tRanscriptomic data) to identify SL pairs. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in accurately identifying SL pairs that are therapeutically actionable in stomach and breast cancers.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.357640v1?rss=1

Authors: Mandal, S., Uhlmann, V.

Abstract:

We present SplineDist, an instance segmentation convolutional neural network for bioimages extending the popular StarDist method. While StarDist describes objects as star-convex polygons, SplineDist uses a more flexible and general representation by modelling objects as planar parametric spline curves. Based on a new loss formulation that exploits the properties of spline constructions, we can incorporate our new object model in StarDist's architecture with minimal changes. We demonstrate in synthetic and real images that SplineDist produces segmentation outlines of equal quality than StarDist with smaller network size and accurately captures non-star-convex objects that cannot be segmented with StarDist.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.359232v1?rss=1

Authors: Lovering, R. C., Gaudet, P., Acencio, M. L., Ignatchenko, A., Jolma, A., Fornes, O., Kuiper, M., Kulakovskiy, I. V., Laegreid, A., Martin, M. J., Logie, C.

Abstract:

DNA-binding transcription factors recognise genomic addresses, specific sequence motifs in gene regulatory regions, to control gene transcription. A complete and reliable catalogue of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such a catalogue of proteins is demonstrated by the many lists of DNA-binding transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. In addition, this curation effort has led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.359828v1?rss=1

Authors: Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., Dogan, T.

Abstract:

Data-centric approaches have been utilized to develop predictive methods for elucidating uncharacterized aspects of proteins such as their functions, biophysical properties, subcellular locations and interactions. However, studies indicate that the performance of these methods should be further improved to effectively solve complex problems in biomedicine and biotechnology. A data representation method can be defined as an algorithm that calculates numerical feature vectors for samples in a dataset, to be later used in quantitative modelling tasks. Data representation learning methods do this by training and using a model that employs statistical and machine/deep learning algorithms. These novel methods mostly take inspiration from the data-driven language models that have yielded ground-breaking improvements in the field of natural language processing. Lately, these learned data representations have been applied to the field of protein informatics and have displayed highly promising results in terms of extracting complex traits of proteins regarding sequence-structure-function relations. In this study, we conducted a detailed investigation over protein representation learning methods, by first categorizing and explaining each approach, and then conducting benchmark analyses on; (i) inferring semantic similarities between proteins, (ii) predicting ontology-based protein functions, and (iii) classifying drug target protein families. We examine the advantages and disadvantages of each representation approach over the benchmark results. Finally, we discuss current challenges and suggest future directions. We believe the conclusions of this study will help researchers in applying machine/deep learning-based representation techniques on protein data for various types of predictive tasks. Furthermore, we hope it will demonstrate the potential of machine learning-based data representations for protein science and inspire the development of novel methods/tools to be utilized in the fields of biomedicine and biotechnology.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.359612v1?rss=1

Authors: DuPai, C. D., Davies, B. W., Wilke, C. O.

Abstract:

The beta hairpin motif is a ubiquitous protein structural motif that can be found in molecules across the tree of life. This motif, which is also popular in synthetically designed proteins and peptides, is known for its stability and adaptability to broad functions. Here we systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank (PDB) to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched inter-strand contacts. We also establish a set of broad design principles that can be applied to the generation of libraries encoding proteins or peptides containing beta hairpin structures.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.356907v1?rss=1

Authors: Heller, D., Vingron, M.

Abstract:

Motivation: With the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes. Results: We introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores on two recently published assemblies of the HG002 individual. Availability and Implementation: SVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/svim-asm. Contact: [email protected] Supplementary information: Supplementary data are available online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.357517v1?rss=1

Authors: Courtney, A. J., Lewis, Z. A.

Abstract:

Neurospora crassa has been an important model organism for molecular biology and genetics for over 60 years. N. crassa has a complex life cycle, with over 28 distinct cell types and is capable of transcriptional responses to many environmental conditions including nutrient availability, temperature, and light. To quantify variation in N. crassa gene expression, we analyzed public expression data from 97 conditions and calculated the Shannon Entropy value for Neurospora's approximately 11,000 genes. Entropy values can be used to estimate the variability in expression for a single gene over a range of conditions and be used to classify individual genes as constitutive or condition-specific. Shannon entropy has previously been used measure the degree of tissue specificity of multicellular plant or animal genes. We use this metric here to measure variable gene expression in a microbe and provide this information as a resource for the N. crassa research community. Finally, we demonstrate the utility of this approach by using entropy values to identify genes with constitutive expression across a wide range of conditions and to identify genes that are activated exclusively during sexual development.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.357202v1?rss=1

Authors: Sztyler, T., Malone, B.

Abstract:

Motivation: We propose a system that learns consistent representations of biological entities, such as proteins and diseases, based on a knowledge graph and additional data modalities, like structured annotations and free text describing the entities. In contrast to similar approaches, we explicitly incorporate the consistency of the representations into the learning process. In particular, we use these representations to identify novel proteins associated with diseases; these novel relationships could be used to prioritize protein targets for new drugs. Results: We show that our approach outperforms state-of-the-art link prediction algorithms for predicting unknown protein-disease associations. Detailed analysis demonstrates that our approach is most beneficial when additional data modalities, such as free text, are informative. Availability: Code and data are available at: https://github.com/nle-sztyler/research-doubler

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