Search Results for "memory"

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...

miRPV is an Automated tool that allows users to predict and validate microRNA from genome/gene sequence. System Requirement CPU: AMD64 (64bit) Memory: 2Gb RAM Storage: 5Gb Ubuntu 18.04

Bisulfite sequencing (BSseq) processing is among the most cumbersome next generation sequencing (NGS) applications. Though some BSseq processing tools are available, they are scattered, require puzzling parameters and are running-time and memory-usage demanding. We have developed P3BSseq, a parallel processing pipeline for fast, accurate and automatic analysis of BSseq reads that trims, aligns, annotates, records the intermediate results, performs bisulfite conversion quality assessment, generates BED methylome and report files following the NIH standards. P3BSseq outperforms the known BSseq mappers regarding running time, computer hardware requirements. ...

SAT-Assembler is a scalable and accurate gene assembly tool for large-scale RNA-Seq and metagenomic data. It recovers genes from gene families of particular interest to biologists with high coverage, low chimera rate, and extremely low memory usage compared with exiting gene assembly tools. Moreover, it is naturally compatible with parallel computing platforms.

...Functions include: duplicate removal demultiplexing primer-removal quality-assurance (QA) graphing quality control (QC) consensus-generation minority-variant determination minority-variant graphing The main current version is 6.X, which is written in Python3. 7.X is my rewrite in Java, but is still work in progress. Both are written to be as lightweight as possible so they can run with minimal memory-requirements on a desktop or laptop as well as on a compute cluster. If you have any problems with QUASR, please do contact me at the email address provided in the README.