26,943 research outputs found
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction.Comment: 13 pages, 5 figure
Representing and analysing molecular and cellular function in the computer
Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model
Integration of Biological Sources: Exploring the Case of Protein Homology
Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration
Domain-mediated interactions for protein subfamily identification
Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.11Ysciescopu
Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation
Peer reviewedPublisher PD
Multi-task Deep Neural Networks in Automated Protein Function Prediction
In recent years, deep learning algorithms have outperformed the state-of-the
art methods in several areas thanks to the efficient methods for training and
for preventing overfitting, advancement in computer hardware, the availability
of vast amount data. The high performance of multi-task deep neural networks in
drug discovery has attracted the attention to deep learning algorithms in
bioinformatics area. Here, we proposed a hierarchical multi-task deep neural
network architecture based on Gene Ontology (GO) terms as a solution to protein
function prediction problem and investigated various aspects of the proposed
architecture by performing several experiments. First, we showed that there is
a positive correlation between performance of the system and the size of
training datasets. Second, we investigated whether the level of GO terms on GO
hierarchy related to their performance. We showed that there is no relation
between the depth of GO terms on GO hierarchy and their performance. In
addition, we included all annotations to the training of a set of GO terms to
investigate whether including noisy data to the training datasets change the
performance of the system. The results showed that including less reliable
annotations in training of deep neural networks increased the performance of
the low performed GO terms, significantly. We evaluated the performance of the
system using hierarchical evaluation method. Mathews correlation coefficient
was calculated as 0.75, 0.49 and 0.63 for molecular function, biological
process and cellular component categories, respectively. We showed that deep
learning algorithms have a great potential in protein function prediction area.
We plan to further improve the DEEPred by including other types of annotations
from various biological data sources. We plan to construct DEEPred as an open
access online tool.Comment: 19 pages, 4 figures, 4 table
Functional Bias and Spatial Organization of Genes in Mutational Hot and Cold Regions in the Human Genome
The neutral mutation rate is known to vary widely along human chromosomes,
leading to mutational hot and cold regions. We provide evidence that categories
of functionally-related genes reside preferentially in mutationally hot or cold
regions, the size of which we have measured. Genes in hot regions are biased
toward extra-cellular communication (surface receptors, cell adhesion, immune
response, etc.) while those in cold regions are biased toward essential
cellular processes (gene regulation, RNA processing, protein modification,
etc.). From a selective perspective, this organization of genes could minimize
the mutational load on genes that need to be conserved and allow fast evolution
for genes that must frequently adapt. We also analyze the effect of gene
duplication and chromosomal recombination, which contribute significantly to
these biases for certain categories of hot genes. Overall, our results show
that genes are located non-randomly with respect to hot and cold regions,
offering the possibility that selection acts at the level of gene location in
the human genome.Comment: 17 pages, 6 figures, 2 tables. accepted to PLOS Biology, Feb. 2004
issu
- …