Search Results for "multiple sequence alignment"

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 “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. ...

...While still very useful, these approaches have several limitations: Most lncRNAs are less conserved and tend to be lineage specific which greatly limit the discrimination power of alignment-based methods. For example, of 550 lncRNAs detected from zebrafish, only 29 of them had detectable sequence

TI2BioP allows mainly the calculation of topological indices (spectral moments) derived from inferred and artificial 2D structures of DNA, RNA and proteins being possible to carry out a structure-function correlation irrespective of sequence alignments. TI2BioP version 3.0 is a python platform with a graphical interface designed for Windows, Linux and Mac OS.

Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. ...

Maximum Common Genome Alignment (MCGA) Tool MCGA is a bioinformatics analysis tool written in Python for generating core genome alignment for bacterial whole genome sequences which can be used to construct phylogenetic trees.

ADOMA stands for: Alternative Display Of Multiple Alignment. ADOMA can create four different displays of a multiple sequence alignment: a ClustalW alignment in HTML format, a simplified ClustalW alignment in HTML and/or txt format and a colored ClustalW alignment in HTML format. For examples of these outputfiles check the screenshots. ADOMA uses ClustalW to create the multiple alignment from DNA or protein sequences and displays them slightly different than the normal output of ClustalW. ...

Multiple mapping reads with a unique location after filtering are rescued and reallocated to the most reliable candidate location. FineSplice requires Python 2.x (>= 2.6) with the following modules installed: pysam (http://code.google.com/p/pysam/) and scikit-learn (http://scikit-learn.org/). For further details check out our publication: Nucl.

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the...

PepT-IDE is a protein analysis tool that is used for multiple sequence alignment, 3D visualization and displaying protein contact maps for protein sequences and structures. It also has feedback communication between the different views of the protein.

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.

trimAl can consider several parameters, alone or in multiple combinations, in order to select the most-reliable positions in the alignment. These include the proportion of sequences with a gap, the level of residue similarity and, if several alignments for the same set of sequences are provided, the consistency level of columns among alignments. Moreover, trimAl allows to manually select a set of columns and sequences to be removed from the alignment.