Search Results for "example"

The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples, and documentation to help you use the AlphaGenome API. AlphaGenome offers multimodal predictions, encompassing diverse functional outputs such as gene expression, splicing patterns, chromatin features, and contact maps. The model analyzes DNA sequences of up to 1 million base pairs in length and can...

...The difference can be very large if your protein has large disordered regions, which are very likely to produce clashes. BioEmu outputs structures in backbone frame representation. To reconstruct the side-chains, several tools are available. As an example, we interface with HPacker to conduct side-chain reconstruction and also provide basic tooling for running a short molecular dynamics (MD) equilibration.

...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

...Start using CodonU today and see how it can revolutionize your genomics research! Find more at: https://github.com/SouradiptoC/CodonU Documentation link: https://souradiptoc.github.io/CodonU Example Link: https://github.com/SouradiptoC/CodonU/tree/master/Examples

MiModD is a software package for genomic variant identification from next-generation sequencing (NGS) data with optimized usage of system resources and a user-friendly interface. For most model organism genomes it lets the user carry out a complete analysis from unaligned genomic NGS read data to an annotated list of variants on a regular Desktop PC within a few hours. Its user-interface is beginner-friendly and designed to encourage geneticists to analyze NGS data themselves without the...

...The aims of FusionCatcher are: - very good detection rate for finding candidate fusion genes, - very easy to use (i.e. no a priori knowledge of databases and bioinformatics is needed in order to run FusionCatcher), - very good detection of challenging fusion genes, like for example IGH fusions, CIC fusions, DUX4 fusions, CRLF2 fusions, TCF3 fusions, etc. - 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.

...AMBIENT (Active Modules for Bipartite Networks) is a Python module that uses simulated annealing to find areas of a metabolic network (modules) that have some consistent characteristic. AMBIENT does not require predefined pathways and gives highly specific predictions of affected areas of metabolism. For example, scores for reactions based on transcriptional data of their annotated encoding genes can be used in the network and modules of coordinated expression changes can be found. This provides an alternative to pathway/gene set enrichment analyses which is simultaneously more flexible and more specific.