Souza, B. | bioRxiv Science

bioRxiv2018

THEA: A novel approach to gene identification in phage genomes

MotivationCurrently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap, and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present THEA (The Algorithm), a novel method for gene calling specifically designed for phage genomes. While the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use graph theory to find the optimal path.\n\nResultsWe compare THEA to other gene callers by annotating a set of 2,133 complete phage genomes from GenBank, using THEA and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with THEA predicting significantly more genes than the other three. We searched for these extra genes in both GenBanks non-redundant protein database and sequence read archive, and found that they are present at levels that suggest that these are functional protein coding genes.\n\nAvailability and ImplementationThe source code and all files can be found at: https://github.com/deprekate/THEA\n\nContactKatelyn McNair: deprekate@gmail.com

bioinformatics↗

bioRxiv2017

On information metrics for spatial coding

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannons mutual information (Shannon, 1948), and convey information rate in bits/sec or bits/spike (Skaggs et al., 1993; Skaggs et al., 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice.

neuroscience↗