PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.23.263749v1?rss=1

Authors: Zhao, X., Hu, A. C., Wang, S., Wang, X.

Abstract:

We describe UVC (https://github.com/genetronhealth/uvc), an open-source method for calling small somatic variants. UVC is aware of both unique molecular identifiers (UMIs) and the tumor-matched normal sample. UVC utilizes the following power-law universality that we discovered: allele fraction is inversely proportional to the cubic root of variant-calling error rate. Moreover, UVC utilizes pseudo-neural network (PNN). PNN is similar to deep neural network but does not require any training data. UVC outperformed Mageri and smCounter2, the state-of-the-art UMI-aware variant callers, on the tumor-only datasets used for publishing these two variant callers. Also, UVC outperformed Mutect2 and Strelka2, the state-of-the-art variant callers for tumor-normal pairs, on the Genome-in-a-Bottle somatic truth sets. UVC outperformed Mutect2 and Strelka2 on 21 in silico mixtures simulating 21 combinations of tumor purity and normal purity. Performance is measured by using sensitivity-specificity trade off for all called variants. The improved variant calls generated by UVC from previously published UMI-based sequencing data are able to provide additional biological insight about DNA damage repair. The versatility and robustness of UVC makes it a useful tool for variant calling in clinical settings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.261735v1?rss=1

Authors: Moller, A. F., Natarajan, K. N.

Abstract:

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.23.263061v1?rss=1

Authors: Shokrof, M., Brown, C. T., Mansour, T. A.

Abstract:

Background: Specialized data structures are required for online algorithms to efficiently handle large sequencing datasets. The counting quotient filter (CQF), a compact hashtable, can efficiently store k-mers with a skewed distribution. Result: Here, we present the mixed-counters quotient filter (MQF) as a new variant of the CQF with novel counting and labeling systems. The new counting system adapts to a wider range of data distributions for increased space efficiency and is faster than the CQF for insertions and queries in most of the tested scenarios. A buffered version of the MQF can offload storage to disk, trading speed of insertions and queries for a significant memory reduction. The labeling system provides a flexible framework for assigning labels to member items while maintaining good data locality and a concise memory representation. These labels serve as a minimal perfect hash function but are ~10 fold faster than BBhash, with no need to re-analyze the original data for further insertions or deletions. Conclusion: The MQF is a flexible and efficient data structure that extends our ability to work with high throughput sequencing data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.260109v1?rss=1

Authors: M. A. Basher, A. R., Hallam, S. J.

Abstract:

We present reMap (relabeling multi-label pathway data based on bag approach), a simple, and yet, generic framework, that performs relabeling examples to a different set of labels, characterized as bags. A bag is comprised of a subset of correlated pathways, and a pathway is allowed to be mixed over bags, constituting an overlapping pathway over a subset of bags. Bag based approach was followed to overcome low sensitivity scores of triUMPF for the pathway prediction task. The relabeling process in reMap is achieved by alternating between 1) assigning bags to each sample and 2) updating reMap's parameters. reMap's effectiveness was evaluated on metabolic pathway prediction where resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved sensitivity score.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.261024v1?rss=1

Authors: Mori, K., Ozaki, H., Fukunaga, T.

Abstract:

Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-1 error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarone's correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarone's correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://github.com/ko-ichimo-ri/MotiMul.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.261818v1?rss=1

Authors: Meyer, J. G.

Abstract:

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies, which are produced by cleavage of all isolated protein by a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false for biological or technical reasons. Here, I describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. Simple linear models are used to assess whether the slope of a peptide's change across treatment groups differs from the slope of all other peptides assigned to the same protein. Reanalysis of proteomic data from primary mouse microglia with PeCorA revealed that about 15% of proteins contain one discordant peptide. Inspection of the discordant peptides shows utility of PeCorA for direct and indirect detection of regulated PTMs, and also for discovery of poorly quantified peptides that should be excluded. PeCorA can be applied to an arbitrary list of quantified peptides, and is freely available as a script written in R.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.255885v1?rss=1

Authors: Zheng, H., Zhu, M. S., Liu, Y.

Abstract:

Circulating cell-free DNA (cfDNA) is a promising biomarker for the diagnosis and prognosis of many diseases, including cancer. The genome-wide non-random fragmentation pat-terns of cfDNA are associated with the nucleosomal protection, epigenetic environment, and gene expression in the cell types that contributed to cfDNA. However, current progress on the development of computational methods and understanding of molecular mechanisms behind cfDNA fragmentation patterns is significantly limited by the controlled-access of cfDNA whole-genome sequencing (WGS) dataset. Here, we present FinaleDB (FragmentatIoN AnaLysis of cEll-free DNA DataBase), a comprehensive database to host thousands of uniformly processed and curated de-identified cfDNA WGS datasets across different pathological conditions. Furthermore, FinaleDB comes with a fragmentation genome browser, from which users can seamlessly integrate thousands of other omics data in different cell types to experience a comprehensive view of both gene-regulatory landscape and cfDNA fragmentation patterns. Availability and implementation: FinaleDB service: http://finaledb.research.cchmc.org/. FinaleDB source code: https://github.com/epifluidlab/finaledb_portal and https://github.com/epifluidlab/finaledb_workflow.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.253245v1?rss=1

Authors: Gooden, A. A., Evans, C. N., Sheets, T. P., Clapp, M. E., Chari, R.

Abstract:

With the technology's accessibility and ease of use, CRISPR has been employed widely in many different organisms and experimental settings. As a result, thousands of publications have used CRISPR to make specific genetic perturbations, establishing in itself a resource of validated guide RNA sequences. While numerous computational tools to assist in the design and identification of candidate guide RNAs exist, these are still just at best predictions and generally, researchers inevitably will test multiple sequences for functional activity. Here, we present dbGuide (https://sgrnascorer.cancer.gov/dbguide), a database of functionally validated guide RNA sequences for CRISPR/Cas9-based knockout in human and mouse. Our database not only contains computationally determined candidate guide RNA sequences, but of even greater value, over 4000 sequences which have been functionally validated either through direct amplicon sequencing or manual curation of literature from over 1000 publications. Finally, our established framework will allow for continual addition of newly published and experimentally validated guide RNA sequences for CRISPR/Cas9-based knockout as well as incorporation of sequences from different gene editing systems, additional species, and other types of site-specific functionalities such as base editing, gene activation, repression, and epigenetic modification.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264747v1?rss=1

Authors: Iakovlev, M., Faravelli, S., Becskei, A.

Abstract:

Exclusive stochastic gene choice combines precision with diversity. This regulation enables most T-cells to express exactly one T-cell receptor isoform chosen from a large repertoire, and to react precisely against diverse antigens. Some cells express two receptor isoforms, revealing the stochastic nature of this process. A similar regulation of odorant receptors and protocadherins enable cells to recognize odors and confer individuality to cells in neuronal interaction networks, respectively. We explored whether genes in other families are expressed exclusively by analyzing single cell RNA-seq data with a simple metric. Chromosomal segments and families are more likely to express genes concurrently than exclusively, possibly due to the evolutionary and biophysical aspects of shared regulation. Nonetheless, gene families with exclusive gene choice were detected in multiple cell types, most of them are membrane proteins involved in ion transport and cell adhesion, suggesting the coordination of these two functions. Thus, stochastic exclusive expression extends beyond the prototypical families, permitting precision in gene choice to be combined with the diversity of intercellular interactions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264721v1?rss=1

Authors: Raikova, R., Krasteva, V., Krutki, P., Drzymała-Celichowska, H., Krysciak, K., Celichowski, J.

Abstract:

Oscillations of muscle force, observed as physiological tremors, rely upon the synchronized firings of active motor units (MUs). This study aimed to investigate the effects of synchronizing the firings of three types of MUs on force development using a mathematical model of the rat medial gastrocnemius muscle. The model was designed based on the actual proportion and physiological properties of MUs and motoneurons innervating the muscle. The isometric muscle and MU forces were simulated by a model predicting non-synchronized firing of a pool of 57 MUs (including eight slow, 23 fast resistant to fatigue, and 26 fast fatigable) to ascertain a maximum excitatory signal when all MUs were recruited into the contraction. The mean firing frequency of each MU depended upon the twitch contraction time, whereas the recruitment order was determined according to increasing forces (the size principle). The synchronization of firings of individual MUs was simulated using four different modes and inducing the synchronization of firings within three time windows ({+/-} 2, {+/-} 4, and {+/-} 6 ms) for four different combinations of MUs. The synchronization was estimated using two parameters, the correlation coefficient and the cross-interval synchronization index. The four scenarios of synchronization increased the values of the root-mean-square, range, and maximum force in correlation with the increase of the time window. Greater synchronization index values resulted in higher root-mean-square, range, and maximum of force outcomes for all MU types as well as for the whole muscle output; however, the mean spectral frequency of the forces decreased, whereas the mean force remained nearly unchanged. The range of variability and the root-mean-square of forces were higher for fast MUs than for slow MUs; meanwhile, the relative values of these parameters were highest for slow MUs, indicating their important contribution to muscle tremor, especially during weak contractions.

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