We established the in silico pipeline to systematically and accurately detect the secretome by combining multiple prediction algorithms for the N-terminal signal peptide and transmembrane domain within the protein sequence.
Computationally determining transcription factor binding sites is a challenging process and multiple prediction algorithms have been developed over the last decade (Cartharius 2005, Wu 2009, Mathelier 2013).
However, we anticipate that even in the aforementioned situations, confidence would still be increased by the inclusion of multiple prediction algorithms.
The sequence views display miRNA target site annotations from multiple prediction algorithms allowing the user to dynamically toggle between them for easy comparison.
A commercial software package called Alamut (Interactive Biosoftware, Rouen, France) integrates multiple reliable, regularly updated data sources and multiple prediction algorithms (for splicing signal detection, PWM, MaxEntScan, NNSplice, GeneSplicer, and HSF are included).
For the two missense variants in NAGLU, we used multiple prediction algorithms, including six from dbNSFP [ 48] (SIFT, PolyPhen, LRT, Mutation Taster, Mutation Assessor, FATHMM), two meta-scores built by us (MetaSVM, MetaLR), and the Tute score (tutegenomics.com), and found that these algorithms gave consistent predictions.
Conventionally, most genome sequences are annotated with multiple gene prediction algorithms such as GLIMMER, CRITICA, and GeneMark, or by manual assignment based on BLAST search results [ 1, 2].
To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms.
The miRGator analysis tool (available at http://genome.ewha.ac.kr/miRGator/miRNAexpression.html), an online interface that uses multiple target prediction algorithms, was then used to identify unknown downstream targets of miR-146a.
The RTX sequence was evaluated using a consensus web-based tool, AmylPred, which utilizes multiple parallel prediction algorithms to define regions of sequence that may contribute to amyloid or amyloid-like packing and association.
The accompanying annotation was produced by transferring the MIPS v3.2 annotation, then refining the gene model annotation integrating multiple gene prediction algorithms within the Maker2 software [ 21] as well as by using available RNA-seq from two wild type F. graminearum isolates (PH-1 and Gz-3639) to provide additional gene model support.
