PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225193v1?rss=1

Authors: Mendez, M., FANTOM Consortium Main Contributors,, Scott, M. S., Hoffman, M. M.

Abstract:

BACKGROUND: Exploratory analysis of complex transcriptomic data presents multiple challenges. Many methods often rely on preexisting gene annotations, impeding identification and characterization of new transcripts. Even for a single cell type, comprehending the diversity of RNA species transcribed at each genomic region requires combining multiple datasets, each enriched for specific types of RNA. Currently, examining combinatorial patterns in these data requires time-consuming visual inspection using a genome browser. METHOD: We developed a new SAGA method, SegRNA, that integrates data from multiple transcriptome profiling assays. SegRNA identifies recurring combinations of signals across multiple datasets measuring the abundance of transcribed RNAs. Using complementary techniques, SegRNA builds on the Segway SAGA framework by learning parameters from both the forward and reverse DNA strands. SegRNA's unsupervised approach allows exploring patterns in these data without relying on pre-existing transcript models. RESULTS: We used SegRNA to generate the first unsupervised transcriptome annotation of the K562 chronic myeloid leukemia cell line, integrating multiple types of RNA data. Combining RNA-seq, CAGE, and PRO-seq experiments together captured a diverse population of RNAs throughout the genome. As expected, SegRNA annotated patterns associated with gene components such as promoters, exons, and introns. Additionally, we identified a pattern enriched for novel small RNAs transcribed within intergenic, intronic, and exonic regions. We applied SegRNA to FANTOM6 CAGE data characterizing 285 lncRNA knockdowns. Overall, SegRNA efficiently summarizes diverse multi-experiment data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.224121v1?rss=1

Authors: Nomi, T., Inoue, S., Fujita, H., Sadamitsu, K., Sakaguchi, M., Tenma, A., Nakagami, H.

Abstract:

B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. They can inhibit their functioning by binding antibodies to antigen proteins. Predicting of epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.218826v1?rss=1

Authors: Lin, Y.-T., Sheu, S.-Y., Lin, C.-C.

Abstract:

Background: Traditional drug development is time-consuming and expensive, while computer-aided drug repositioning can improve efficiency and productivity. In this study, we proposed a machine learning pipeline to predict the binding interaction between proteins and marketed or studied drugs. We then extended the predicted interactions to construct a protein network that could be applied to discover the potentially shared drugs between proteins and thus predict drug repositioning. Methods: Binding information between proteins and drugs from the Binding Database and the physicochemical properties of drugs from the ChEMBL database were used to build the machine learning models, i.e. support vector regression. We further measured proportionalities between proteins by the predicted binding affinity and introduced edge betweenness centrality to construct a protein similarity network for drug repositioning. Results: As the proof of concept, we demonstrated our machine learning approach is capable of reflecting the binding strength between drugs and the target protein. When comparing coefficients of protein models, we found proteins SYUA and TAU that may share common ligand which were not in our training data. Using the edge betweenness centrality network based on the prediction proportionality of protein models, we found a potential target, AK1C2, of aspirin and of which the binding interaction had been validated. Conclusions: Our study could not only be applied to drug repositioning by comparing protein models or searching the protein-protein network, but also to predict the binding strength once the sufficient experimental data was provided to train the protein models.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.224022v1?rss=1

Authors: Kong, S., Kubatko, L.

Abstract:

Interspecific hybridization is an important evolutionary phenomenon that generates genetic variability in a population and fosters species diversity in nature. The availability of large genome scale datasets has revolutionized hybridization studies to shift from the examination of the presence or absence of hybrids in nature to the investigation of the genomic constitution of hybrids and their genome-specific evolutionary dynamics. Although a handful of methods have been proposed in an attempt to identify hybrids, accurate detection of hybridization from genomic data remains a challenging task. The available methods can be classified broadly as site pattern frequency based and population genetic clustering approaches, though the performance of the two classes of methods under different hybridization scenarios has not been extensively examined. Here, we use simulated data to comparatively evaluate the performance of four tools that are commonly used to infer hybridization events: the site pattern frequency based methods HyDe and the D-statistic (i.e., the ABBA-BABA test), and the population clustering approaches structure and ADMIXTURE. We consider single hybridization scenarios that vary in the time of hybridization and the amount of incomplete lineage sorting (ILS) for different proportions of parental contributions ({gamma} ); introgressive hybridization; multiple hybridization scenarios; and a mixture of ancestral and recent hybridization scenarios. We focus on the statistical power to detect hybridization, the false discovery rate (FDR) for the D-statistic and HyDe, and the accuracy of the estimates of {gamma} as measured by the mean squared error for HyDe, structure, and ADMIXTURE. Both HyDe and the D-statistic demonstrate a high level of detection power in all scenarios except those with high ILS, although the D-statistic often has an unacceptably high FDR. The estimates of {gamma} in HyDe are impressively robust and accurate whereas structure and ADMIXTURE sometimes fail to identify hybrids, particularly when the proportional parental contributions are asymmetric (i.e., when {gamma} is close to 0). Moreover, the posterior distribution estimated using structure exhibits multimodality in many scenarios, making interpretation difficult. Our results provide guidance in selecting appropriate methods for identifying hybrid populations from genomic data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.216879v1?rss=1

Authors: Saha, S., Soliman, A., Rajasekaran, S.

Abstract:

Nowadays we are observing an explosion of gene expression data with phenotypes. It enables researchers to efficiently identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffers from being very underdetermined system. In a very large set of features but a very small sample size domains (e.g., DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several different spurious feature subsets may yield equally optimal results. This phenomenon is known as "instability". Considering these facts, we have developed a very robust and stable supervised gene selection algorithm to select the most discriminating non-spurious set of genes from the gene expression datasets with phenotypes. "Stability" and "robustness" is ensured by class and instance levels perturbations, respectively. We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. It revealed that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also done biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.221465v1?rss=1

Authors: Li, D., Ding, J., Bar-Joseph, Z.

Abstract:

Motivation: Recent technological advances enable the profiling of spatial single cell expression data. Such data presents a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of this data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. Results: We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. Availability: MESSI is available at: https://github.com/doraadong/MESSI.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.224527v1?rss=1

Authors: Durairaj, J., Melillo, E., Bouwmeester, H. J., Beekwilder, J., de Ridder, D., van Dijk, A. D.

Abstract:

Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.224071v1?rss=1

Authors: Durrant, M. G., Bhatt, A. S.

Abstract:

Recent work performed by Sberro et al. (2019) revealed a vast unexplored space of small proteins existing within the human microbiome. At present, these small open reading frames (smORFs) are unannotated in existing reference genomes and standard genome annotation tools are not able to accurately predict them. In this study, we introduce an annotation tool named SmORFinder that predicts small proteins based on those identified by Sberro et al. This tool combines profile Hidden Markov models (pHMMs) of each small protein family and deep learning models that may better generalize to smORF families not seen in the training set. We find that combining predictions of both pHMM and deep learning models leads to more precise smORF predictions and that these predicted smORFs are enriched for Ribo-Seq or MetaRibo-Seq translation signals. Feature importance analysis reveals that the deep learning models learned to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codons in a way that strongly corresponds to the codon synonyms found in the codon table. We perform a core genome analysis of 26 bacterial species and identify many core smORFs of unknown function. We pre-compute small protein annotations for thousands of RefSeq isolate genomes and HMP metagenomes, and we make these data available through a web portal along with other useful tools for small protein annotation and analysis. The systematic identification and annotation of those important small proteins will help researchers to expand our understanding of this exciting field of biology.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.224584v1?rss=1

Authors: Meleshko, D., Korobeynikov, A.

Abstract:

COVID-19 pandemic has ignited a broad scientific interest in coronavirus research. The identification of coronaviridae species in natural reservoirs often requires de novo assembly. However, existing transcriptome assemblers often are not able to assemble coronaviruses into a single contig. We developed coronaSPAdes, a new module for SPAdes assembler for coronavirus species recovery. coronaSPAdes uses the knowledge about coronaviridae genome structure to improve assembly. We have shown that coronaSPAdes outperforms existing SPAdes modes and other popular short-read assemblers in the recovery of full-length coronavirus genomes. This should allow to better understand the coronaviridae spread and diversity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225078v1?rss=1

Authors: Perez-Pulido, A. J., Asencio-Cortes, G., Brokate-Llanos, A. M., Brea-Calvo, G., Rodriguez-Grinolo, R., Garzon, A., Munoz, M. J.

Abstract:

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data now allows the design of secondary analyses that take advantage of this information to create new knowledge through specific computational approaches. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed ASACO, Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of normalized and heterogeneous transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and even common transcriptional regulators in any biological model, including human diseases or microbial infections. The present SARS-CoV-2 pandemic is an opportunity to test this novel approach due to the wealth of data that is being generated, which could be used for validating results. In addition, new cell mechanisms identified could become new therapeutic targets. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV and/or SARS-CoV-2 and searched for genes tightly co-expressed with them. We have found around 1900 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlap with those identified by former laboratory high-throughput screenings (with p-value near 0). Some of these genes aim to cellular structures such as the stress granules, which could be essential for the virus replication and thereby could constitute potential targets in the current fight against the virus. Additionally, our results reveal a series of common transcription regulators, involved in immune and inflammatory responses, that might be key virus targets to induce the coordinated expression of SARS-CoV-2 host factors. All of this proves that ASACO can discover gene co-regulation networks with potential for proposing new genes, pathways and regulators participating in particular biological systems.

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