TCP Experiment Automation Controlled Using Python
TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. When multiple values are provided (e.g. for TCP congestion control algorithm), an experiment is made up of multiple tests. For each experiment and test, TEACUP collects a range of data, such as tcpdump files of traffic seen or TCP stack information (e.g. using Web10G). TEACUP also collects a variety of metadata from the end hosts and bottleneck router, such as the actual OS/kernel version(s) used. TEACUP also provides some simple tools for analysing the results of experiments, such as plotting a flow's experienced RTT over time.
Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.
Non-Coding RNA PROfiling from sRNA-seq
ncPRO-seq is a tool for annotation and profiling of ncRNAs from smallRNA sequencing data. It aims to interrogate and perform detailed analysis on small RNAs derived from annotated non-coding regions in miRBase, piRBase, Rfam and repeatMasker, and regions defined by users. The ncPRO pipeline also has a module to identify regions significantly enriched with short reads that can not be classified as known ncRNA families. ############# Docker version : download and run Dockerfile (go in "Files" section) ############# GitHub : https://github.com/jbrayet/ncpro-seq
ALEXA-Seq is a method for using massively parallel paired-end transcriptome sequencing for 'alternative expression analysis'.
A pipeline to define allele-specific genomic features
Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here we present Allelome.PRO, an automated userfriendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analysed imprinted expression to give a complete picture of the “allelome” by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased biallelic or noninformative. Allelome.PRO offers increased sensitivity to analyse lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data.
QUASI is a toolkit to rapidly assess the quality of shRNA-Seq based data and call differential abundance using common statistical inference methods (DESeq, edgeR, baySeq).
Pipeline and set of tools for SNP calling and classification
SNP-SACK provides a complete ''de novo'' pipeline for the calling of variants in any organism and a set of tools for the classification of this variants according to their level of conservation. SNP-SACK was conceived and designed for the efficient characterisation and classification of SNPs and other markers in pseudo-diploid organisms.
In this very first part of the project OpenSpaceSyntax is a collection of functions in shall scripts oriented to the calculation of classic measures of a space syntax analisys procedure for urban fabrics and based on GIS-GRASS (grass.itc.it).
An End-to-End Analysis Pipeline for BS-seq
BSpipe is a comprehensive pipeline from sequence quality control and mapping to functional analysis of differentially methylated regions: (1) sequencing quality assessment, (2) sequence cleaning, (3) sequence read mapping, (4) methylation quantification, (4) sample comparisons based on methylation profile, (5) identification of DMRs (differentially methylated regions), (6) annotation of DMRs, (7) functional analysis of differentially methylated genes, (8) generation of input files for visualization, and (9) support for advanced sequencing technologies such as TAB-seq, OxBS-seq, MAP-it, and NOMe-seq.
Criação/Manutenção/Utilização de banco de dados da MegaSena.
Conjunto de scripts e aplicativos para criação, manutenção e utilização de banco de dados da MegaSena extraído de arquivo público fornecido pelo website da Caixa Econômica Federal.
Simulation of allele-specific RNA-seq data
Simulation of allele-specific RNA-seq data
spinal cord DTI pre-processing
Shell script for spinal cord diffusion tensor imaging (DTI) preprocessing and outlier rejection.
My personal utilities wrote for dealing with Science and Computational Chemistry Jobs.