Reproducibility-FBB-MSU

Ab-Initio-Protein-Structure-Prediction

Using materials and methods of the article "Ab initio protein structure prediction of CASP III targets using ROSETTA" written by Kim T. Simons, Rich Bonneau, Ingo Ruczinski and David Baker, we conducted ab initio folding to generate structures consistent with both the local and nonlocal interactions responsible for protein stability. To generate structures consistent we've implemented 3, 5 and 9 residue fragments of known structures into local sequences similar to the target sequence using a Monte Carlo simulated annealing procedure with further refinement of the obtained complete tertiary structure according to the article.

CAMISIM_reproducibility

To reproduce some results from "CAMISIM: simulating metagenomes and microbial communities". Authors: Simon Konnov, Ciara Makievskaya, Ruslan Gumerov

Phenograph

Single cell RNA-seq and clusterization (phenograph)

Cell-Cycle-at-Single-Cell-Resolution

To reproduce some results from "Cell-cycle dynamics of chromosomal organization at single-cell resolution" (Nagano et al, Nature, 2017)

CRISPR-Cas3

Reproducibility of some plots from "Cas3-Derived Target DNA Degradation Fragments Fuel Primed CRISPR Adaptation" paper (Künne et al., 2016 Molecular Cell) for the purpose of learning Python for computational biology.

Genome-Expansion

To reproduce some plots from the article "Evolutionary Rewiring of Human Regulatory Networks by Waves of Genome Expansion" (Marnetto et al, Am J Hum Genet, 2018)

GOATOOLS_repr

Воспроизведение некоторых результатов из статьи про GOATOOLS: A Python library for Gene Ontology analyses (DOI:10.1038/s41598-018-28948-z)

Host-Taxon-Predictor

A Tool for Predicting Taxon of the Host of a Newly Discovered Virus

instructions

This repo contains instructions for the students about git usage and projects update.

Ribo-seq

Reproducibility of some plots from "Clarifying the Translational Pausing Landscape in Bacteria by Ribosome Profiling" paper (Mohammad et al., 2016 Cell Reports) for the purpose of learning Python for computational biology.

RNA-DNA

Reproducibility of some plots from GRID-Seq paper (Xiao Li et al., 2018 Nature Biotechnology) for the purpose of learning Python for computational biology.

ScScope

Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning

Single-cell-Alzheimer

To reproduce some results from "A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease" (H. Keren-Shaul et al, Cell, 2017)

Splicing-factor-1-modulates-dietary-restriction-and-TORC1-pathway-longevity-in-_C.-elegans_

Re-analysis of the publication

TADs

Reproducibility of some plots from "Comparison of computational methods for Hi-C data analysis" paper (Forcato et al., 2017 Nature Methods) for the purpose of learning Python for computational biology.

treeWAS

Test of treeWAS tool described in the paper "A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination" (Caitlin Collins, Xavier Didelot, 2018, PLOS Computational Biology).

uORF

Application-of-Uncertainpy-for-Izhikevich-Neuron-Model-Quantification

Authors: Anton Izzi, Stanislav Tikhonov, Anton Vlasov. In this project we used Uncertainpy module for Python to quantify Izhikevich neuron models with different parameters to evaluate the module's quality.

CichlidsGeneFlow

Воспроизведение Fig 3. из статьи Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow

codon_bias

Convolutional-neural-networks_ncRNA

To reproduce some results from "Convolutional neural networks for classification of alignments of non-coding RNA sequences"

Evolutionarily-informed-deep-learning

Авторы: Бердникович Е, Раздобарин З., Третьяков Д.

FreePSI

Трифонова, Волобуева

Giggle

Reproducibility of visualization of GIGGLE scores from the relationships between 15 genomic states across different cell types and tissues predicted by ChromHMM for Roadmap and MyoD ChIP-seq peaks

QIIME2-mock-community-evaluations

Korkunova, Nesterenko, Raldugina. Taxonomic classification of marker-gene sequences is an important step in microbiome analysis.