- What are the analyses and functions available in LegumeIP V3?
- How many species are available in LegumeIP V3?
- How do I start using LegumeIP V3?
- What is a genomic feature?
- What are the definitions of Experiment, Condition and Sample in the RNA_seq data for gene expression analysis?
- Which ontology are supported for gene functional annotation in the LegumeIP V3?
- Is there a quick way to directly query expression levels under common conditions?
- Suppose that I have some RNA-seq data. Can I upload into LegumeIP V3 by myself?
- How often is LegumeIP V3 updated?
- What is the IT technology behind the LegumeIP V3?
- In co-expression analysis, what is beta value and scale-free topology index plot? How to adjust it?
What are the analyses and functions available in LegumeIP V3?
LegumeIP V3 is an integrative Gene Discovery Platform in legumes. This database hosts large-scale genomics and transcriptomics data and provides multiple bioinformatics tools for the study of gene functions and evolutions in 22 species, including two outgroup reference species - Arabidopsis thaliana and Populus trichocarpa. Since the genomic data from multiple species can be loaded, it is possible to perform comparative genomic analysis.
Here is the list of analyses and functions available in LegumeIP V3:
Search, browse and download features by functional keywords, coordinates, identifiers or gene expression patterns.
Perform gene expression analysis, including:
- Gene expression profiles: users can select and normalize RNA-seq samples. Also, users can compare expression levels of any gene(s) or transcript(s) over selected samples, download expression values for all genes, display expression charts or search genes by expression patterns over samples.
- Differential expression analysis: users can find genes differentially expressed between two conditions.
- Co-expression analysis.
Pathway enrichment analysis: users can utilize GO or KEGG databases.
Map genomic features (for example genes or transcripts) from one genome to another one by Homology or PANTHER classification.
Search gene family by functional keywords, gene ontology terms (especially PANTHER terms) or number of gene family members.
BLAST search (NCBI).
Sequence Cutter: users can cut sequences based on genomic sequences, names or coordinates.
How many species are available in LegumeIP V3?
LegumeIP V3 hosts 22 plant genomes, including multiple annotations for Glycine max (Gm275Wm82a2V1 and Gm508Wm82a4V1) and Medicago truncatula (MtV4.0 and MtrunA17r5.0).
How do I start using LegumeIP V3?
First, users need to select one species of interest among the 22 available in LegumeIP V3. Click on the species of your interest on the Home page or Organism page. Then, you are ready to use all different analyses and functions available.
What is a genomic feature?
In bioinformatics, genomic feature is a term to describe any genomic region with some annotated function.
Thus, a genomic feature can be a chromosome, contig, scaffold, gene, transcript, CDS, exon, intron or UTR. In LegumeIP V3, protein/multipeptide are also considered genomic features.
What are the definitions of Experiment, Condition and Sample in the RNA_seq data for gene expression analysis?
- LegumeIP V3 hosts RNA-seq data for gene expression analysis. The same batch of RNA-seq data are grouped into a experiment. Usually, these data are sampled and sequenced in the same experimental design and submitted or published together.
- Condition can also be called as Treatment, and is a group of RNA-seq data from the same experiment. These data share the same biological treatment.
- Sample is the basic unit of RNA-seq data. A sample is a biological replicate in a condition.
Which ontology are supported for gene functional annotation in the LegumeIP V3?
LegumeIP V3 annotates genes using KEGG, KO, GO, InterProScan and PANTHER.
Is there a quick way to directly query expression levels under common conditions?
Yes, we curated expression data and generated
pre-defined datasets for each genome.
The content of pre-defined datasets depends on the availability of published RNA-seq data.
Typical example can be a group of tissue specific RNA-seq samples such as root, flower and leaf, etc.
or a group of samples with typical biotic or abiotic stress treatment.
To utilize these pre-defined datasets, firstly choose
organism on left panel of home page, then choose
from top navbar, you will see a list of predefined dataset with
Session ID and
Simply click the session ID link, page will be redirected to final result. You can check expression value and see expression chart
once you input gene name by keywords or coordinates. Furthermore, you can search genes satisfying a specific expression pattern.
Suppose that I have some RNA-seq data. Can I upload into LegumeIP V3 by myself?
The answer is no. But, our team can help you to upload your data as long as we have the genomic data included. However, please note that your data will become publicly available after the uploading.
How often is LegumeIP V3 updated?
LegumeIP V3 will be updated periodically once new genome annotations and RNA-seq data are available.
What is the IT technology behind the LegumeIP V3?
LegumeIP V3 is developed on Python Flask and MySQL. The database also utilizes PANDAS and Bioconductor R packages, such as DESeq2 and WGCNA.
In co-expression analysis, what is beta value and scale-free topology index plot? How to adjust it?
LegumeIP V3 adopts WGCNA for co-expression clustering analysis. The algorithm need the beta value to adjust granula of clustering. Backend pipeline will automacally decide the best beta value for your dataset.
However, you may prefer to adjust it, especially when your dataset cover multiple experiements.
For this purpose, check
co-expression result page >
Correlation coefficients between genes >
Scale-free topology index plot figure to choose suitable beta value.
Please refer to here (question 6)
and here for more details.
Once you decide to change beta value, click
Option on step-by-step top bar to enter new beta value and submit your session again.