6,597 research outputs found
Automated DNA Motif Discovery
Ensembl's human non-coding and protein coding genes are used to automatically
find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular
expressions (RE) is used by genetic programming to ensure the generated strings
are legal. The evolved motif suggests the presence of Thymine followed by one
or more Adenines etc. early in transcripts indicate a non-protein coding gene.
Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems
biology, machine learning, Bioinformatics, motif, regular expression, strongly
typed genetic programming, context-free grammar.Comment: 12 pages, 2 figure
A new procedure to analyze RNA non-branching structures
RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)
Dynamics of gene expression and the regulatory inference problem
From the response to external stimuli to cell division and death, the
dynamics of living cells is based on the expression of specific genes at
specific times. The decision when to express a gene is implemented by the
binding and unbinding of transcription factor molecules to regulatory DNA.
Here, we construct stochastic models of gene expression dynamics and test them
on experimental time-series data of messenger-RNA concentrations. The models
are used to infer biophysical parameters of gene transcription, including the
statistics of transcription factor-DNA binding and the target genes controlled
by a given transcription factor.Comment: revised version to appear in Europhys. Lett., new titl
Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data.
Non-sequential models such as SVMs or feed-forward neural networks are often
used although they have no natural way of handling sequences of varying length.
Recurrent neural networks such as the long short term memory (LSTM) model on
the other hand are designed to handle sequences. In this study we demonstrate
that LSTM networks predict the subcellular location of proteins given only the
protein sequence with high accuracy (0.902) outperforming current state of the
art algorithms. We further improve the performance by introducing convolutional
filters and experiment with an attention mechanism which lets the LSTM focus on
specific parts of the protein. Lastly we introduce new visualizations of both
the convolutional filters and the attention mechanisms and show how they can be
used to extract biological relevant knowledge from the LSTM networks
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
- …