STUMPY is a powerful and scalable Python library for modern time series analysis

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

golang library for computing matrix profiles along with other time series analysis features

A performant, powerful query framework to search for network motifs

Dimension UI is a desktop application designed to collect, store, visualize, and analyze time series data

Motiflets

Fast motif matching in R

Time-series analysis using the Matrix profile in Julia

LoCoMotif is a time series motif discovery method that discovers variable-length motif sets in multivariate time series using time warping

pyJASPAR: A Pythonic interface to JASPAR transcription factor motifs

Time Series Motif Detection in Python

A motif discovery tool to detect the occurrences of known motifs

Bayesian Markov Model motif discovery tool version 2 - An expectation maximization algorithm for the de novo discovery of enriched motifs as modelled by higher-order Markov models.

Variational Auto Encoders for learning binding signatures of transcription factors

Classify time series data using motifs discovered from Sequitur processing of SAX discretized data.

Yet Another Model Using Neural Networks for Predicting Binding Preferences of for Test DNA Sequences

PEnG-motif is an open-source software package for searching statistically overrepresented motifs (position specific weight matrices, PWMs) in a set of DNA sequences.

A unified framework for discovering, analyzing, integrating, and visualizing regulatory motifs and transcription factor binding sites across bulk, single-cell, and long-read sequencing modalities.

:m: Tool for motif conservation analysis

An R package for de novo discovery of enriched DNA motifs (e.g. TFBS)

The matrix profile data structure and associated algorithms for mining time series data

Structural Temporal Modeling to characterize temporal networks

The TSMD project brings together Motif Discovery methods for Time Series, aiming to compare their performance through well-defined research questions and to simplify their practical use. It provides both guidelines for selecting the most suitable methods based on the data, and accessible implementations of the most relevant approaches.

A proof-of-concept library for motif analysis using MDL techniques.

Time Series with Matrix Profile in Java

Motif discovery for DNA sequences using multiobjective optimization and genetic programming.

R package for the RNA Centric Annotation System (RCAS)

:whale: :computer: Docker recipe for meme-suite

Motif discovery done by AI algorithms and python.