iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC

BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble

A terminator is a DNA region that ends the transcription process. Currently, multiple computational tools are available for predicting bacterial terminators. However, these methods are specialized for certain bacteria or terminator type (i.e. intrinsic or factor-dependent). In this work, we developed BacTermFinder using an ensemble of convolutional neural networks (CNNs) receiving as input four different representations of terminator sequences. To develop BacTermFinder, we collected roughly 41 000 bacterial terminators (intrinsic and factor-dependent) of 22 species with varying GC-content (from 28% to 71%) from published studies that used RNA-seq technologies. We evaluated BacTermFinder's performance on terminators of five bacterial species (not used for training BacTermFinder) and two archaeal species. BacTermFinder's performance was compared with that of four other bacterial terminator prediction tools. Based on our results, BacTermFinder outperforms all other four approaches in terms of average recall without increasing the number of false positives. Moreover, BacTermFinder identifies both types of terminators (intrinsic and factor-dependent) and generalizes to archaeal terminators. Additionally, we visualized the saliency map of the CNNs to gain insights on terminator motif per species. BacTermFinder is publicly available at https://github.com/BioinformaticsLabAtMUN/BacTermFinder.

Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation

All sequencing projects of bacteriophages (phages) should seek to report an accurate and comprehensive annotation of their genomes. This article defines 14 questions for those new to phage genomics that should be addressed before submitting a genome sequence to the International Nucleotide Sequence Database Collaboration or writing a publication.

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

BACKGROUND

The origin is the starting site of DNA replication, an extremely vital part of the informational inheritance between parents and children. More importantly, accurately identifying the origin of replication has great application value in the diagnosis and treatment of diseases related to genetic information errors, while the traditional biological experimental methods are time-consuming and laborious.

RESULTS

We carried out research on the origin of replication in a variety of eukaryotes and proposed a unique prediction method for each species. Throughout the experiment, we collected data from 7 species, including Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Kluyveromyces lactis, Pichia pastoris and Schizosaccharomyces pombe. In addition to the commonly used sequence feature extraction methods PseKNC-II and Base-content, we designed a feature extraction method based on TF-IDF. Then the two-step method was utilized for feature selection. After comparing a variety of traditional machine learning classification models, the multi-layer perceptron was employed as the classification algorithm. Ultimately, the data and codes involved in the experiment are available at https://github.com/Sarahyouzi/EukOriginPredict .

CONCLUSIONS

The prediction accuracy of the training set of the above-mentioned seven species after 100 times fivefold cross validation reach 92.60%, 90.80%, 91.22%, 96.15%, 96.72%, 99.86%, 96.72%, respectively. It denotes that compared with other methods, the methods we designed could accomplish superior performance. In addition, our experiments reveals that the models of multiple species could predict each other with high accuracy, and the results of STREME shows that they have a certain common motif.