Search Results for "input-leap"

Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as...

- Molecular Docking Virtual Screening Tool To Aid In Drug Discovery Research. - Published in Wiley, Journal of Computational Chemistry . Link: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27390 - Developed with Synchronized functioning of Python and Batch scripts -Integerated With Pymol-open-source for visualizing interaction (PSE file) generated from MzDOCK - Integrated with Molecule Drawing Tool - JSME Editor - If you are facing any issues or for queries . Contact us -...

CrossMap is a program for convenient conversion of genome coordinates and genomeannotation files between assemblies (eg. lift from GRCh36/hg18 to GRCh37/hg19 or vice versa).It support file in BAM, SAM, BED, Wiggle, BigWig, GFF, GTF format.

... - to be as automatic as possible (i.e. the FusionCatcher will choose automatically the best parameters in order to find candidate fusion genes, e.g. finding automatically the adapters, building the exon-exon junctions automatically based on the length of the input reads, etc.) while providing the best possible detection rate for finding fusion genes.

...The pipeline has been validated and streamlined by comparing its predictions to manually curated gene models in three fungal genomes, and its results show substantial increases in sensitivity and selectivity over previous gene predictions. Sensitivity is gained by repeatedly running the HMM gene predictor Augustus with varied input parameters, and selectivity by choosing the models with best homology to known proteins and best agreement to the RNA-Seq data. SnowyOwl has successfully predicted genes in 26 novel fungal genomes. The pipeline can be installed locally for high throughput and control over configuration. It can also be run on a remote server through a convenient web interface for occasional use.

ChIP-RNA-seqPRO: A strategy for identifying regions of epigenetic deregulation associated with aberrant transcript splicing and RNA-editing sites. Runnable python scripts packaged together with customized annotation libraries, demo data input and README guide. 9/26 : v1.1 Updated MAIN_IV to debug error thrown by python pandas no longer supporting 'subset'. This code will no longer be actively maintained/updated here. A cloud-based resource for comparative analysis of epigenetic, sequence variation, and expression datasets is now available. Please visit the Cloudomics, project for cloud-based resources: https://sourceforge.net/projects/cloudomics-for-aws/

PhyloTrack is a JavaScript--based software tool that integrates the D3.js library for data visualization with the JBrowse tool for genome browser representation. It requires a phylogenetic tree of the common Newick data format as input, as well as three meta data files for samples, clade-defining nodes and clade color definitions - all in tab delimited format. Functionality within PhyloTrack shows the informative markers at each node in the phylogenetic tree, therefore highlighting clade-defining polymorphism. This functionality has been implemented using the tabix tool on the server side, providing simple and rapid access to the information at each tree node, including informative SNPs stored in VCF-similar files. ...

MDcons is a tool to analyze conserved contacts during Molecular Dynamics (MD) simulations of Protein, Rna, Dna & Ligand based complexes. The input is either a Molecular Dynamics trajectory or a set of snapshots. The input can also be a single snapshot. The outputs are (1) map of most/less frequently conserved contacts during MD (2) a list of most/less frequently conserved contacts during MD.

FineSplice is a Python wrapper to TopHat2 geared towards a reliable identification of expressed exon junctions from RNA-Seq data, at enhanced detection precision with small loss in sensitivity. Following alignment with TopHat2 using known transcript annotations, FineSplice takes as input the resulting BAM file and outputs a confident set of expressed splice junctions with the corresponding read counts. Potential false positives arising from spurious alignments are filtered out via a semi-supervised anomaly detection strategy based on logistic regression. Multiple mapping reads with a unique location after filtering are rescued and reallocated to the most reliable candidate location. ...

An input GUI for parameterizing simulation with the LSD Simulator

This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (

Biological Annotation Tool is a general-purpose high speed environment for manipulating biological sequence annotations in multiple input and output formats. A plugin-style API permits much extensibility.

...Moreover, trimAl allows to manually select a set of columns and sequences to be removed from the alignment. trimAl implements a series of automated algorithms that trim the alignment searching for optimum thresholds based on inherent characteristics of the input alignment, to be used so that the signal-to-noise ratio after alignment trimming phase is increased. Among trimAl’s additional features, trimAl allows getting the complementary alignment (columns that were trimmed), to compute statistics from the alignment, to select the output file format , to get a summary of trimAl’s trimming in HTML and SVG formats, and many other options.