MAFin: Motif Detection in Multiple Alignment Files
Motivation: Genome and Proteome Alignments, represented by the Multiple Alignment File (MAF) format, have become a standard approach in the field of comparative genomics and proteomics. However, current approaches lack a direct method for motif detection within MAF files. To address this gap, we present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files, streamlining genomic and proteomic research. Results: We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: 1) by using user-specified k-mers to search the sequences. 2) with regular expressions, in which case one or more patterns are searched, and 3) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enable the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses. Availability: MAFin is released as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin. Contact: izg5139@psu.edu
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involves a pre-trained embedding model, which …
RAGRetrievalRetrieval-augmented GenerationThe Mean Partition Theorem of Consensus Clustering
To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We …
ClusteringFinding Motif Sets in Time Series
Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is…
Time SeriesTime Series AnalysisSequential visibility-graph motifs
Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamic…
Time SeriesTime Series AnalysisThe Origin of Motif Families in Food Webs
Food webs have been found to exhibit remarkable motif profiles, patterns in the relative prevalences of all possible three-species sub-graphs, and this has been related to ecosystem properties such as stability and robus…