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This Month in Bioinformatics- Research Updates of May 2021

Bioinformatics Review

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This Month in Bioinformatics- Research Updates of May 2021

In this article, we summarize the latest interesting findings made this month in bioinformatics.

1. A new software for detection and quantification of genome editing translocation from NGS data

New software called CRISPECTOR is developed for accurate estimation of genome editing translocation and off-target activity from NGS data [1]. It performs statistical analysis of NGS data from multiplex-PCR comparative experiments. Later, it quantifies the translocation events. CRISPECTOR is a command-line tool that is available as a Bioconda package at http://bioconda.github.io/recipes/crispector/README.html. The source can be found at GitHub.

For more information, read here.

2. A new suite to model cis-regulatory elements for motif occurrence combinatorics

A newly expanded suite is developed to model the motif occurrence combinatorics in DNA sequences [2]. This suite is known as MOCCA (Motif Occurrence Combinatorics Algorithms). It is based on support vector machines (SVM) and random forest (RF) models, SVM-MOCCA and RF-MOCCA respectively. MOCCA is implemented in C++ with a minimal number of dependencies to ease the process of installation. It can be installed on Unix-based systems.

For more information, read here.

3. An updated version of IPD to analyze the SARS-CoV-2 genome

IPD2.0 [3] is an updated version of the Infectious Pathogen Detector (IPD) [4] is developed. It helps in deriving insights from the SARS-CoV-2 genome. IPD is a computational pipeline that consists of an analysis module of SARS-CoV-2. This module performs genomic analysis that helps in understanding the dynamics and variability of the virus. The SARS-CoV-2 module generates SARS-CoV-2 genomic samples and works on phylogenetic clade analysis. It is written in Python3 and the desktop version is available for Linux requiring Conda and Tkinter for its installation.

For more information, read here.

4. Novel tool to discover DNA motifs associated with CpG methylation events

A new tool called CpGmotifs is developed to find DNA motifs associated with CpG methylation events [5]. CpGmotifs is based on short DNA motif discovery. it retrieves and characterizes DNA patterns related to CpG methylation in the human genome. It is written in R language and comes with an easy GUI. The source code is freely accessible on GitHub along with a docker image available on DockerHub.

For more information, read here.

5. A novel method to accurately prioritize cancer-driving genes

A novel method called driveR is developed to accurately prioritize cancer-driving genes using somatic genomics data [6]. It is based on batch analysis that prioritizes the driver genes using somatic genomics data. The driveR has been tested on 28 datasets and provides the most accurate results as compared to the other standard methods. This package is written in R and is platform-independent. The driveR is freely accessible on CRAN.

For more information, read here.

6. A new tool for manual refinement of 3D microscopy annotations

A new open-source tool called Segmentor is developed that allows manual annotation and refinement of objects from 3D microscopy annotations [7]. It is based on a 2D-3D approach to visualize and segment objects within the 3D image. Segmentor is developed in C++ and is available for Windows, Mac, and Linux. It is freely accessible for non-academic use and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor.

For more information, read here.


References

  1. Amit, I., Iancu, O., Levy-Jurgenson, A. et al. (2021). CRISPECTOR provides accurate estimation of genome editing translocation and off-target activity from comparative NGS data. Nat Commun 12, 3042.
  2. Bredesen, B.A., Rehmsmeier, M. (2021). MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics. BMC Bioinformatics 22, 234.
  3. Desai, S., Rane, A., Joshi, A. et al. (2021). IPD 2.0: To derive insights from an evolving SARS-CoV-2 genome. BMC Bioinformatics 22, 247.
  4. Desai, S., Rashmi, S., Rane, A., Dharavath, B., Sawant, A., & Dutt, A. (2021). An integrated approach to determine the abundance, mutation rate and phylogeny of the SARS-CoV-2 genome. Briefings in bioinformatics22(2), 1065-1075.
  5. Scala, G., Federico, A. & Greco, D. (2021). CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events. BMC Bioinformatics 22, 278.
  6. Ülgen, E., Sezerman, O.U. (2021). driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics 22, 263.
  7. Borland, D., McCormick, C.M., Patel, N.K. et al. Segmentor: a tool for manual refinement of 3D microscopy annotations. BMC Bioinformatics 22, 260 (2021).

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