Unlock your biological data


Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 25 of 25 results
filter_list Filters
language Programming Language
healing Disease
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 25 of 25 results
RRS / Ribosome Release Score
Identifies functional protein-coding transcripts with greater sensitivity by detecting the termination of translation at the end of an open reading frame. RRS is well-suited to distinguish real translation from non-ribosomal contamination since it is robust to potential protection by non-ribosomal proteins as such protection should show no bias for the presence of a stop codon. It provides a valuable metric to prioritize candidates for more in-depth characterization.
CNCI / Coding-Non-Coding Index
A powerful signature tool by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan.
COME / COding potential calculation tool based on Multiple fEatures
Predicts the coding potential for a given transcript. COME integrates multiple sequence-derived and experiment-based features using a decompose-compose method, which makes COME’s performance more accurate and robust than other well-known tools. First, COME composes the feature matrix for the given transcripts using the pre-calculated features vectors. Second, COME predicts the coding potential by the pre-trained models, using the feature matrix generated in the first step.
PLncPRO / Plant Long Non-Coding RNA Prediction by Random fOrest
Discovers long non-coding RNAs (lncRNAs) in plants via classifying coding and long non-coding transcripts. PLncPRO allows prediction of lncRNAs in plants based on various sequence features extracted from the training data using random forest (RF) algorithm. The software was used to predict lncRNAs in two crop plants, chickpea and rice, under abiotic stress conditions, and the lncRNAs identified can provide a resource to elucidate their exact function in abiotic stress responses in future studies.
A method to determine whether a multi-species nucleotide sequence alignment is likely to represent a protein-coding region. It does not rely on homology to known protein sequences; instead, it examines evolutionary signatures characteristic to alignments of conserved coding regions, such as the high frequencies of synonymous codon substitutions and conservative amino acid substitutions, and the low frequencies of other missense and non-sense substitutions (CSF = Codon Substitution Frequencies).
RRE / Retrieval Regulative Elements
Allows the extraction of any genomic region surrounding annotated coding sequence and its upload on a MySql database. RRE is a parser that is suitable for users wishing to generate genome-wide, specific sequence-feature datasets, such as putative promoters, first non-coding exon, all introns of a specific chromosome or contig. The RRE database is used to retrieve annotated putative regulative regions as well as the non-coding regions linked to orthologue annotations.
Identifies the long non-coding RNAs (lncRNAs) from the assembled novel transcripts. lncScore can also be used to calculate the coding potential. This alignment-free tool uses a logistic regression model with 11 carefully selected features. lncScore accurately distinguishes lncRNAs from mRNAs, especially partial-length mRNAs in the human and mouse datasets. In addition, lncScore also performed well on transcripts from five other species (Zebrafish, Fly, C. elegans, Rat, and Sheep). To speed up the prediction, multithreading is implemented within lncScore.
Provides a prototype noncoding RNA genefinder, based on comparative genome sequence analysis. QRNA detects conserved RNA secondary structures, including both ncRNA genes and cis-regulatory RNA structures. It uses three different probabilistic models (for RNA-structure-constrained, coding-constrained, and position-independent evolution) to examine the pattern of mutations in a pairwise sequence alignment. The alignment is classified as RNA, coding, or other, according to the Bayesian posterior probability of each model. This program is freely available for download.
CNCTDiscriminator / Coding and Non-coding Transcript Discriminator
A coding and noncoding transcript discriminating system where we applied the integration of four categories of features about the transcripts: i) base compositions, (ii) ORF statistics, (iii) transcript expression scores and (iv) properties of the secondary structure of the transcripts. The feature integration was done using both hypothesis learning and feature specific ensemble learning approaches. The CNCTDiscriminator model which was trained with composition and ORF features outperforms (precision 83.86%, recall 82.01%) other three popular methods -- CPC (precision 98.31%, recall 25.95%), CPAT (precision 97.74%, recall 52.50%) and PORTRAIT (precision 84.37%, recall 73.2%) when applied to an independent benchmark dataset. However, the CNCTDiscriminator model that was trained using the ensemble approach shows comparable performance (precision 89.85%, recall 71.08%).
FEELnc / FlExible Extraction of LncRNAs
An alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. FEELnc also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs.
GPGA / Gene Prediction with Genetic Algorithm
A gene prediction technique based on Genetic Algorithm (GA) for determining the optimal positions of exons of a gene in a chromosome or genome. GPGA reduces the problem of correct identification of the coding and non-coding regions by searching only one exon at a time instead of all exons along with its introns. The advantage of this representation is that it can break the entire gene-finding problem into a number of smaller subspaces and thereby reducing the computational complexity. It is used in the analysis of large, unknown eukaryotic genomic sequences by mapping with known genes. It can be utilized as a tool for identifying a gene optimally in a large genomic sequence. The GPGA can be utilized in mapping of a genome with genes present in several well-known repositories like Ensemble, UCSC browser and others.
A web application for identifying long non-coding RNAs (lncRNAs). DeepLNC takes calibrated k-mer frequencies of lncRNAs and coding transcript sequences as its computational features. The k-mer based-features and implication of deep neural network (DNN) algorithm were used to build a binary classification model to separate lncRNAs from mRNAs. The classification model achieved high accuracy rate of (98.07 %) on the training dataset (where k-mer combination was 2, 3, 5) with tenfold cross-validation. The proposed DNN algorithm efficiently handles non-linearity in data using fewer parameters and better hierarchical layer-wise function compression. It facilitates global error correction within multiple weight layers with the use of accelerated gradient learning algorithm.
0 - 0 of 0 results
1 - 2 of 2 results
filter_list Filters
computer Job seeker
Disable 1
thumb_up Fields of Interest
public Country
language Programming Language
1 - 2 of 2 results

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.