PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371112v1?rss=1

Authors: Periwal, V., Bassler, S., Andrejev, S., Gabrielli, N., Typas, A., Patil, K. R.

Abstract:

Natural products constitute a vast yet largely untapped resource of molecules with therapeutic properties. Computational approaches based on structural similarity offer a scalable approach for evaluating their bioactivity potential. However, this remains challenging due to the immense structural diversity of natural compounds and the complexity of structure-activity relationships. We here assess the bioactivity potential of natural compounds using random forest models utilizing structural fingerprints, maximum common substructure, and molecular descriptors. The models are trained with small-molecule drugs for which the corresponding protein targets are known (1,410 drugs, 0.9 million pairs). Using these models, we evaluated circa 11k natural compounds for functional similarity with therapeutic drugs (1.7 million pairs). The resulting natural compound-drug similarity network consists of several links with support from the published literature as well as links suggestive of unexplored bioactivity of natural compounds. As a proof of concept, we experimentally validated the model-predicted Cox-1 inhibitory activity of 5-methoxysalicylic acid, a compound commonly found in tea, herbs and spices. In contrast, a control compound, with the highest similarity score when using the most weighted fingerprint metric, did not inhibit Cox-1. Our results illustrate the importance of complementing structural similarity with the prior data on molecular interactions, and presents a resource for exploring the therapeutic potential of natural compounds.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.370858v1?rss=1

Authors: Popitsch, N., Preuner, S., Lion, T.

Abstract:

Clinical decision making is increasingly guided by accurate and recurrent determination of presence and frequency of (somatic) variants and their haplotype through panel sequencing of disease-relevant genomic regions. Haplotype calling (phasing), however, is difficult and error prone unless variants are located on the same read which limits the ability of short-read sequencing to detect, e.g., co-occurrence of drug-resistance variants. Long-read panel sequencing enables direct phasing of amplicon variants besides having multiple other benefits, however, high error rates of current technologies prevented their applicability in the past. We have developed nanopanel2 (np2), a variant caller for Nanopore panel sequencing data. Np2 works directly on base-called FAST5 files and uses allele probability distributions and several other filters to robustly separate true from false positive calls. It effectively calls SNVs and INDELs with variant allele frequencies (VAF) as low as 1% and 5% respectively and produces only few low-frequency false-positive calls. Haplotype compositions are then determined by direct phasing. Np2 is the first somatic variant caller for Nanopore data, enabling accurate, fast (turnaround <48h) and cheap (sequencing costs ~10$/sample) diagnostic workflows.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.370528v1?rss=1

Authors: Lin, j., Liu, G., Han, Z., Gao, Q., Wang, Z., Chang, Z., Gao, Y., Ma, D.

Abstract:

Backgroundl Xingnaojing injection (XNJ) is extracted from the Chinese ancient prescription "An-Gong-Niu-Huang Pill", is widely used for stroke in China. We mainly observe the effect of XNJ (Xingnaojing) injection on the gut microbiota in stroke model mice. Methods: Forty-two 7- to 8-week-old male C57 mice weighing 22-24 g were chosen for the experiment. There were 6 mice in each group; the 7 groups were the normal group (NG), the MCAO group (CG), the MCAO+XNJ group (EG), the sham surgery group (SG), the sham germ-free normal group (SGFNG), the sham germ-free+MCAO group (SGFCG), and the sham germ-free+MCAO+XNJ group (SGFEG). Two days before modeling, we abdominally administered Xingnaojing (6 mg/kg) the SGFEG and EG groups. The processing time of sustained XNJ was 5 days. Three days after modeling, 1 ~ 2 mouse feces were collected, and after a MiSeq PE library was constructed, an Illumina MiSeq PE 300 platform was used for high-throughput sequencing. After cleaning the sequencing data, the microbiome and microbiomeseq packages were used for analysis using R software (version 3.6.2). Results: Alpha diversity analysis revealed that the diversity was not different between the CG and EG. The Simpson index was different between the SGFCG and SGFEG. XNJ increased the levels of Sutterellaceae and decreased the level of Deferribacteres and Morganella. LEfSe analysis showed that SGFCG mice were also enriched with Morganella. XNJ increased the concentrations of the SCFAs PA (propionate), VA (valerate), IBA (isobutyrate), and IVA (isovalerate) in the feces of the SGFEG group. BA (butyrate) had greater positive correlation with gut bacteria than other acids in the SGFCG, and XNJ changed this trend. KEGG analysis showed that peptidoglycan biosynthesis was most different between the CG and EG. Conclusion: Ischemic stroke (IS) causes dysbiosis of some specific bacteria in the gut microbiota in MCAO mice. Xingnaojing ameliorated this condition by increasing the levels of Sutterellaceae and decreasing the level of Deferribacteres and Morganella. These results are in accordance with other research on Chinese medicines for IS that affect the gut microbiota. Enrichment analysis of SCFAs revealed that XNJ improved the levels of SCFAs through an energy metabolism-related pathway.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.372227v1?rss=1

Authors: Hassan, S. S., Pal Choudhury, P., Uversky, V. N., Dayhoff, G. W., Aljabali, A. A. A., Uhal, B., Lundstrom, K., Rezaei, N., Seyran, M., Pizzol, D., Adadi, P., Lal, A., Soares, A., Abd El-Aziz, T. M., Kandimalla, R., Tambuwala, M., Azad, G. K., Sherchan, S. P., Baetas-da-Cruz, W., Takayama, K., Serrano Aroca, A., Chauhan, G., Palu, G., Brufsky, A.

Abstract:

The coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which is pandemic with an estimated fatality rate less than 1% is ongoing. SARS-CoV-2 accessory proteins ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 with putative functions to manipulate host immune mechanisms such as interferons, immune signaling receptor NLRP3 (NOD-, LRR-, and pyrin domain-containing 3) inflammasome, inflammatory cytokines such as interleukin {beta} (IL-1{beta}) are critical in COVID-19 pathology. Outspread variations of each of the six accessory proteins of all complete proteomes (available as of October 26, 2020, in the National Center for Biotechnology Information depository) of SARS-CoV-2, were observed across six continents. Across all continents, the decreasing order of percentage of unique variations in the accessory proteins was found to be ORF3a>ORF8>ORF7a>ORF6>ORF10>ORF7b. The highest and lowest unique variations of ORF3a were observed in South America and Oceania, respectively. This finding suggests that the wide variations of accessory proteins seem to govern the pathogenicity of SARS-CoV-2, and consequently, certain propositions and recommendations can be made in the public interest.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371799v1?rss=1

Authors: Shaw, J., Yu, Y. W.

Abstract:

Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition (MSMTP) problem, which is a more flexible graphical metric compared to the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization (UPEM) model, which is a probabilistic generalization of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably to state-of-the-art algorithms -- up to 30 times faster with 2 times fewer switch errors on 6x ploidy simulated data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.07.372136v1?rss=1

Authors: Safari, A. H., Sedaghat, N., Forna, A., Zabeti, H., Chindelevitch, L., Libbrecht, M.

Abstract:

Drug resistance in Mycobacterium tuberculosis (MTB) may soon be a leading worldwide cause of death. One way to mitigate the risk of drug resistance is through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task featurize the WGS data from a given bacterial isolate by defining one input feature per SNP. Here, we introduce a gene-centric method for predicting drug resistance in TB. We define one feature per gene according to the number of mutations in that gene in a given isolate. This representation greatly decreases the number of model parameters. We further propose a model that considers both gene order through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers. We find that using these strategies yields a substantial, statistically-significant improvement over the state-of-the-art and that this improvement is driven by the order of genes in the genome and their organization into operons.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.371963v1?rss=1

Authors: Weber, L. M., Hippen, A. A., Hickey, P. F., Berrett, K. C., Gertz, J., Doherty, J. A., Greene, C. S., Hicks, S. C.

Abstract:

Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the natural genetic variation. Here, we performed in silico benchmark evaluations by combining sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden, confirming that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design. We demonstrate that this strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.06.369439v1?rss=1

Authors: Strömich, L., Wu, N., Barahona, M., Yaliraki, S. N.

Abstract:

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph theoretical methods: Bond-to-bond propensity analysis, which has been previously successful in identifying allosteric sites without a priori knowledge in benchmark data sets, and, Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. We further score the highest ranking sites against random sites in similar distances through statistical bootstrapping and identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.369587v1?rss=1

Authors: BIANCHETTI, L., Sinar, D., Depenveiller, C., Dejaegere, A.

Abstract:

In vertebrates, the mineralocorticoid receptor (MR) is a steroid-activated nuclear receptor (NR) that plays essential roles in water-electrolyte balance and blood pressure homeostasis. It belongs to the group of oxo-steroidian NRs, together with the glucocorticoid (GR), progesterone (PR), and androgen (AR) receptors. Classically, these oxo-steroidian NRs homodimerize and bind to specific genomic sequences to activate gene expression. NRs are multi-domain proteins, and dimerization is mediated by both the DNA (DBD) and ligand binding (LBD) domains, with the latter thought to provide the largest dimerization interface. However, at the structural level, the LBD dimerization of oxo-steroidian receptors has remained largely a matter of debate. This is linked to the receptor refractory expression, purification and crystallization. As a result, there is currently no consensus on a common homodimer assembly across the 4 receptors, i.e. GR, PR, AR and MR, despite their sequence homology. Examining the available MR LBD crystals and using widely plebiscited tools such as PISA, PRISM and EPPIC, and the MM/PBSA method, we have determined that an interface mediated by the helices H9 and H10 of the LBD as well as by the F domain presents the features of a biological protein-protein interaction surface. This interface which has been observed in both GR and MR crystals, distinguished itself among other contacts and provided for the first time a homodimer architecture that is common to both oxo-steroidian receptors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.370593v1?rss=1

Authors: Roychowdhury, D., Gupta, S., Qin, X., Arighi, C. N., Vijay-Shanker, K.

Abstract:

Motivation: microRNAs (miRNAs) are essential gene regulators and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications, and developing new hypotheses built on previous knowledge. Here, we present emiRIT, a text mining-based resource, which presents miRNA information mined from the literature through a user-friendly interface. Results: We collected 149,233 miRNA-PubMed ID pairs from Medline between January 1997 to May 2020. emiRIT currently contains miRNA-gene regulation (60,491 relations); miRNA-disease (cancer) (12,300 relations); miRNA-biological process and pathways (23,390 relations); and circulatory miRNAs in extracellular locations (3,782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. Conclusion: We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large-scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource's information coverage, in the absence of gold standards, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of diseases. Database URL: https://research.bioinformatics.udel.edu/emirit/

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