PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.222976v1?rss=1

Authors: Everetts, N., Worley, M. I., Yasutomi, R., Yosef, N., Hariharan, I. K.

Abstract:

In both vertebrates and invertebrates, generating a functional appendage requires interactions between ectoderm-derived epithelia and mesoderm-derived cells. To investigate such interactions, we used single-cell transcriptomics to generate a cell atlas of the Drosophila wing disc at two time points during development. Using these data, we investigate gene expression using a multi-layered model of the wing disc and catalogued ligand-receptor pairs that could mediate signaling between epithelial cells and adult muscle precursors (AMPs). We found that localized expression of the FGF ligands, Thisbe and Pyramus, in the disc epithelium regulates the number and location of the AMPs. In addition, Hedgehog ligand from the epithelium activates a specific transcriptional program within adjacent AMP cells, which is critical for proper formation of a subset of the direct flight muscles. More generally, our annotated atlas provides a global view of potential cell-cell interactions between subpopulations of epithelial and myogenic cells.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.26.222265v1?rss=1

Authors: Li, Y., Qiao, B., Olvera de la Cruz, M.

Abstract:

The surface of proteins is vital in determining protein functions. Herein, a program, Protein Surface Printer(PSP), is built that performs multiple functions in quantifying protein surface domains. Two proteins, PETase and cytochrome P450, are used to validate that the program supports atomistic simulations with different combinations of programs and force fields. A case study is conducted on the structural analysis of the spike proteins of SARS-CoV-2 and SARS-CoV, and the human cell receptor ACE2. Although the surface domains of both spike proteins are highly similar, their receptor binding domains (RBDs) and the O-linked glycan domains are structurally different. Statistically, the outer surface of ACE2 displays less correlation with the RBD of SARS-CoV-2 than that of SARS-CoV. The O-linked glycan domain of SARS-CoV-2 is highly positively charged, which may promote binding to negatively charged human cells. Our program paves the way for an accurate understanding of protein binding for aggregation and ligand recognition.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.26.222117v1?rss=1

Authors: Jain, S., Xiao, X., Bogdan, P., Bruck, J.

Abstract:

Evolution is a process of change where mutations in the viral RNA are selected based on their fitness for replication and survival. Given that current phylogenetic analysis of SARS-CoV-2 identifies new viral clades after they exhibit evolutionary selections, one wonders whether we can identify the viral selection and predict the emergence of new viral clades? Inspired by the Kolmogorov complexity concept, we propose a generative complexity (algorithmic) framework capable to analyze the viral RNA sequences by mapping the multiscale nucleotide dependencies onto a state machine, where states represent subsequences of nucleotides and state-transition probabilities encode the higher order interactions between these states. We apply computational learning and classification techniques to identify the active state-transitions and use those as features in clade classifiers to decipher the transient mutations (still evolving within a clade) and stable mutations (typical to a clade). As opposed to current analysis tools that rely on the edit distance between sequences and require sequence alignment, our method is computationally local, does not require sequence alignment and is robust to random errors (substitution, insertions and deletions). Relying on the GISAID viral sequence database, we demonstrate that our method can predict clade emergence, potentially aiding with the design of medications and vaccines.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.220483v1?rss=1

Authors: Rychkov, D., Neely, J., Oskotsky, T., Sirota, M.

Abstract:

Background/Purpose: There is an urgent need to identify effective biomarkers for early diagnosis of Rheumatoid Arthritis (RA) and accurate monitoring of disease activity. Here we define a RA meta-profile using publicly available cross-tissue gene expression data and apply machine learning to identify putative biomarkers, which we further validate on independent datasets. Methods: We carried out a comprehensive search for publicly available microarray gene expression data in the NCBI Gene Expression Omnibus database for whole blood and synovial tissues from RA patients and healthy controls. The raw data from 13 synovium datasets with 284 samples and 14 blood datasets with 1,885 samples were downloaded and processed. The datasets for each tissue were merged and batch corrected and split into training and test sets. We then developed and applied a robust feature selection pipeline to identify genes dysregulated in both tissues and highly associated with RA. From the training data we identified a set of overlapping differentially expressed genes following the condition of co-directionality. The classification performance of each gene in the resulting set was evaluated on the testing sets using AUROC. Five independent datasets were used to validate and threshold the feature selected (FS) genes. Finally, we define the RAScore, composed of a geometric mean of the selected RAScore Panel genes and demonstrate its clinical utility. Results: The result of the feature selection pipeline was a set of 25 upregulated and 28 downregulated genes. To assess the robustness of these feature selected genes, we trained a Random Forest machine learning model with this set of 53 genes and then with the set of 32 common differentially expressed genes and tested on the validation cohorts. The model with FS genes outperformed the model with common DE genes with AUC 0.89 +/- 0.04 vs 0.86 +/- 0.05. The FS genes were further thresholded on the 5 independent datasets resulting in 10 upregulated genes, TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, that are involved in innate immune system pathways, including neutrophil degranulation and apoptosis and expressed in granulocytes, dendritic cells, and macrophages; and 3 downregulated genes, HSP90AB1, NCL, CIRBP, involved in metabolic processes and T-cell receptor regulation of apoptosis and expressed in lymphoblasts. To investigate the clinical utility of the 13 validated genes, the RA Score was developed and found to be highly correlated with DAS28 (r = 0.33 +/- 0.03, p = 7e-9) and able to distinguish OA and RA samples (OR 0.57, 95% CI [0.34, 0.80], p = 8e-10). Moreover, the RA Scores were not significantly different for RF-positive and RF-negative RA sub-phenotypes (p = 0.9) suggesting the generalizability of this score in clinical applications. The RA Score was also able to monitor the treatment effect among RA patients (t-test of treated vs untreated, p = 2e-4) and distinguish polyJIA from healthy individuals in 10 independent pediatric cohorts (OR 1.15, 95% CI [1.01, 1.3], p = 2e-4). Conclusion: The RAScore, consisting of 13 putative biomarkers, identified through a robust feature selection procedure on public data and validated using multiple independent data sets may be useful in the diagnosis and treatment monitoring of RA.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.220335v1?rss=1

Authors: Sayed, M., Park, J. W.

Abstract:

MicroRNAs are small non-coding RNAs that are known for their role in post-transcriptional regulation of target genes. Typically, their functions are predicted by first identifying their target genes and then finding biological processes enriched in these targets. Current tools for miRNA functional analysis use only genes with physical binding sites as their targets and exclude other genes that are indirectly targeted transcriptionally through transcription factors. Here, we introduce a method to predict gene ontology (GO) annotations indirectly targeted by microRNAs. The proposed method resulted in better performance in predicting known miRNA-GO term associations compared to the canonical approach. To facilitate miRNA GO enrichment analysis, we developed an R Shiny application, miRinGO, that is freely available from GitHub at https://github.com/Fadeel/miRinGO

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.220467v1?rss=1

Authors: Dabke, K., Kreimer, S., Jones, M. R., Parker, S. J.

Abstract:

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, there is no single imputation method that is best suited for a diverse range of data sets and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used two distinct DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set and an independent data set with actual experimental samples. We found that imputation methods based on local structures within the data, like local least squares (LLS) and random forest (RF), worked well in our dilution series data set whereas, imputation methods based on global structures within the data, like BPCA performed well in our independent data set. We also found that imputation at the most basic level of protein quantification i.e., fragment level, improved accuracy and number of proteins quantified. Overall, this study indicates that the most suitable imputation method depends on the overall structure and correlations of proteins within the data set and can be identified with the workflow presented here.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.220616v1?rss=1

Authors: Frith, M. C., Noe, L., Kucherov, G.

Abstract:

Analysis of genetic sequences is usually based on finding similar parts of sequences, e.g. DNA reads and/or genomes. For big data, this is typically done via "seeds": simple similarities (e.g. exact matches) that can be found quickly. For huge data, sparse seeding is useful, where we only consider seeds at a subset of positions in a sequence. Here we study a simple sparse-seeding method: using seeds at positions of certain "words" (e.g. ac, at, gc, or gt). Sensitivity is maximized by using words with minimal overlaps. That is because, in a random sequence, minimally-overlapping words are anti-clumped. We provide evidence that this is often superior to acclaimed "minimizer" sparse-seeding methods. Our approach can be unified with design of inexact (spaced and subset) seeds, further boosting sensitivity. Thus, we present a promising approach to sequence similarity search, with open questions on how to optimize it.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.25.220855v1?rss=1

Authors: Wang, L., Qu, L., Yang, L., Wang, Y., Zhu, H.

Abstract:

Nanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. Since 2014, Oxford Nanopore Technologies (ONT) has developed a series of devices based on nanopore sequencing to produce very long reads, with an expected impact on genomics. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Although several basecalling tools have been developed for nanopore sequencing over the past years, it is still challenging to correct the sequences after applying the basecalling procedure. In this study, we developed an open-source DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers. Our results showed NanoReviser, as a post-basecalling reviser, significantly improving the basecalling quality. After being trained on standard ONT sequencing reads from public E. coli and human NA12878 datasets, NanoReviser reduced the sequencing error rate by over 5% for both the E. coli dataset and the human dataset. The performance of NanoReviser was found to be better than those of all current basecalling tools. Furthermore, we analyzed the modified bases of the E. coli dataset and added the methylation information to train our module. With the methylation annotation, NanoReviser reduced the error rate by 7% for the E. coli dataset and specifically reduced the error rate by over 10% for the regions of the sequence rich in methylated bases. To the best of our knowledge, NanoReviser is the first post-processing tool after basecalling to accurately correct the nanopore sequences without the time-consuming procedure of building the consensus sequence. The NanoReviser package is freely available at https://github.com/pkubioinformatics/NanoReviser.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.26.221168v1?rss=1

Authors: Stoffel, M. A., Nakagawa, S., Schielzeth, H.

Abstract:

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest and monitors the change in R2 as a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients are converted to the total variance explained by a predictor, termed 'inclusive' R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomials GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.24.219899v1?rss=1

Authors: Marquet, M., Hölzer, M., Pletz, M. W., Viehweger, A., Makarewicz, O., Ehricht, R., Brandt, C.

Abstract:

Phages are among the most abundant and diverse biological entities on earth. Identification from sequence data is a crucial first step to understand their impact on the environment. A variety of bacteriophage identification tools have been developed over the years. They differ in algorithmic approach, results and ease of use. We, therefore, developed 'What the Phage' (WtP), an easy-to-use and parallel multitool approach for phage identification combined with an annotation and classification downstream strategy, thus, supporting the user's decision-making process when the phage identification tools are not in agreement to each other. WtP is reproducible and scales to thousands of datasets through the use of a workflow manager (Nextflow). WtP is freely available under a GPL-3.0 license (https://github.com/replikation/What_the_Phage).

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