Provided by: seqcluster_1.2.8+ds-2_all 
NAME
seqcluster - seqcluster Documentation [image: seqcluster banner] [image]
Analysis of small RNA sequencing data. It detect unit of transcription over the genome,
annotate them and create an HTML interactive report that helps to explore the data
quickly.
Contents:
INSTALLATION
Seqcluster
With bcbio installed
If you already have
`bcbio`_
, seqcluster comes with it. If you want the last development version:
/bcbio_anaconda_bin_path/seqcluster_install.py --upgrade
Docker:
docker pull lpantano/smallsrna
Bioconda binary
install conda if you want an isolate env:
wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
bash Miniconda-latest-Linux-x86_64.sh -b -p ~/install/seqcluster/anaconda
You can install directly from binstar (only for linux):
~/install/seqcluster/anaconda/conda install seqcluster seqbuster bedtools samtools pip nose numpy scipy pandas pyvcf -c bioconda
With that you will have everything you need for the python package. The last step is to
add seqcluster to your PATH if conda is not already there.
Go to Tools dependecies below to continue with the installation.
Note: After installation is highly recommended to get the last updated version doing:
seqcluster_install.py --upgrade
automated installation
Strongly recommended to use bcbio installation if you work with sequencing data. But if
you want a minimal installation:
pip install fabric
seqcluster_install --upgrade
mkdir -p $PATH_TO_TOOLS/bin
seqcluster_install --tools $PATH_TO_TOOLS
After that you will need to add to your path: export PATH=$PATH_TO_TOOLS/bin:$PATH
Tools dependecies for a full small RNA pipeline
For seqcluster command:
• bedtools
• samtools
• rnafold (for HTML report)
For some steps of a typical small RNA-seq pipeline (recommended to use directly
`bcbio`_
):
• STAR, bowtie
• fastqc
• cutadapt (install with bioconda using the same python env than seqcluster.
You will need to link the cutadapt binary to your PATH)
Data
Easy way to install your small RNA seq data with cloudbiolinux. Seqcluster has snipped
code to do that for you. Recommended to use
`bcbio`_
for the pipeline since will install everything you need in a single step bcbio_nextgen.py
upgrade -u development --tools --genomes hg19 --aligners bowtie.
But If you want to run seqcluster step by step an example of hg19 human version it will be
(another well annotated supported genome is mm10):
Download genome data:
seqcluster_install --data $PATH_TO_DATA --genomes hg19 --aligners bowtie2 --datatarget smallrna
If you want to install STAR indexes since gets kind of better results than bowtie2
(warning, 40GB memory RAM needed):
seqcluster_install --data $PATH_TO_DATA --genomes hg19 --aligners star
R package
Install isomiRs package for R using devtools:
devtools::install_github('lpantano/isomiRs')
To install all packages used by the Rmd report:
Rscript -e 'source(https://raw.githubusercontent.com/lpantano/seqcluster/master/scripts/install_libraries.R)'
CITATION
Please if you use seqcluster make sure to cite the other tools are integrated here:
A non-biased framework for the annotation and classification of the non-miRNA small RNA
transcriptome. Pantano L1, Estivill X, Martí E. Bioinformatics. 2011 Nov 15;27(22):3202-3.
doi: 10.1093/bioinformatics/btr527. Epub 2011 Oct 5. PMID: 21976421
SeqBuster is a bioinformatic tool for the processing and analysis of small RNAs datasets,
reveals ubiquitous miRNA modifications in human embryonic cells. Pantano L, Estivill X,
Martí E. Nucleic Acids Res. 2010 Mar;38(5):e34. Epub 2009 Dec 11.
Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparing
genomic features. Bioinformatics. 26, 6, pp. 841–842.
Dale RK, Pedersen BS, and Quinlan AR. Pybedtools: a flexible Python library for
manipulating genomic datasets and annotations. Bioinformatics (2011).
doi:10.1093/bioinformatics/btr539
Li H.*, Handsaker B.*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G.,
Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence
alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9. [PMID: 19505943]
Li H A statistical framework for SNP calling, mutation discovery, association mapping and
population genetical parameter estimation from sequencing data. Bioinformatics. 2011 Nov
1;27(21):2987-93. Epub 2011 Sep 8. [PMID: 21903627]
GETTING STARTED
Best practices are implemented in a python framework.
clustering of small RNA sequences
seqcluster generates a list of clusters of small RNA sequences, their genome location,
their annotation and the abundance in all the sample of the project [image]
REMOVE ADAPTER
I am currently using cutadapt:
cutadapt --adapter=$ADAPTER --minimum-length=8 --untrimmed-output=sample1_notfound.fastq -o sample1_clean.fastq -m 17 --overlap=8 sample1.fastq
COLLAPSE READS
To reduce computational time, I recommend to collapse sequences, also it would help to
apply filters based on abundances. Like removing sequences that appear only once.
seqcluster collapse -f sample1_clean.fastq -o collapse
Here I am only using sequences that had the adapter, meaning that for sure are small
fragments.
This is compatible with UMI barcodes. If you have in the read name UMI_ATCGAT ``, then the
tool will remove PCR dupiclates as well. To confirm this happened, the tool should output
this sentence during the processing of the file: ``Find UMI tags in read names, collapsing
by UMI.
PREPARE SAMPLES
seqcluster prepare -c file_w_samples -o res --minl 17 --minc 2 --maxl 45
the file_w_samples should have the following format:
lane1_sequence.txt_1_1_phred.fastq cc1
lane1_sequence.txt_2_1_phred.fastq cc2
lane2_sequence.txt_1_1_phred.fastq cc3
lane2_sequence.txt_2_1_phred.fastq cc4
two columns file, where the first column is the name of the file with the small RNA
sequences for each sample, and the second column in the name of the sample.
The fastq files should be like this:
@seq_1_x11
CCCCGTTCCCCCCTCCTCC
+
QUALITY_LINE
@seq_2_x20
TGCGCAGTGGCAGTATCGTAGCCAATG
+
QUALITY_LINE
</pre>
Where _x[09] indicate the abundance of that sequence, and the middle number is the index
of the sequence.
This script will generate: seqs.fastq and seqs.ma. * seqs.fastq: have unique sequences
and unique ids * seqs.ma: is the abundance matrix of all unique sequences in all samples
ALIGNMENT
You should use an aligner to map seqs.fa to your genome. A possibility is bowtie or STAR.
From here, we need a file in BAM format for the next step. VERY IMPORTANT: the BAM file
should be sorted
bowtie -a --best --strata -m 5000 INDEX seqs.fastq -S | samtools view -Sbh /dev/stdin | samtools sort -o /dev/stdout temp > seqs.sort.bam
or
STAR --genomeDir $star_index_folder --readFilesIn res/seqs.fastq --alignIntronMax 1 --outFilterMultimapNmax 1000 --outSAMattributes NH HI NM --outSAMtype BAM SortedByCoordinate
CLUSTERING
seqcluster cluster -a res/Aligned.sortedByCoord.out.bam -m res/seqs.ma -g $GTF_FILE -o res/cluster -ref PATH_TO_GENOME_FASTA --db example
• -a is the SAM file generated after mapped with your tool, which input has been seqs.fa
• -m the previous seqs.fa
• -b annotation files in bed format (see below examples) [deprecated]
• -g annotation files in gtf format (see below examples) [recommended]
• -i genome fasta file used in the mapping step (only needed if -s active)
• -o output folder
• -ref genome fasta file. Needs fai file as well there. (i.e hg19.fa, hg19.fa.fai)
• -d create debug logging
• -s construction of putative precursor (NOT YET IMPLEMENTED)
• --db (optional) will create sqlite3 database with results that will be used to browse
data with html web page (under development)
Example of a bed file for annotation (the fourth column should be the name of the
feature):
chr1 157783 157886 snRNA 0 -
Strongly recommend gtf format. Bed annotation is deprecated. Go here to know how to
download data from hg19 and mm10.
Example of a gtf file for annotation (the third column should be the name of the feature
and the value after gene name attribute is the specific annotation):
chr1 source miRNA 1 11503 . + . gene name 'mir-102' ;
hint: scripts to generate human and mouse annotation are inside seqcluster/scripts folder.
OUTPUTS
• counts.tsv: count matrix that can be input of downstream analyses
• size_counts.tsv: size distribution of the small RNA by annotation group
• seqcluster.json: json file containing all information
• log/run.log: all messages at debug level
• log/trace.log: to keep trace of algorithm decisions
Interactive HTML Report
This will create html report using the following command assuming the output of seqcluster
cluster is at res:
seqcluster report -j res/seqcluster.json -o report -r $GENONE_FASTA_PATH
where $GENOME_FASTA_PATH is the path to the genome fasta file used in the alignment.
Note: you can try our new visualization tool!
• report/html/index.html: table with all clusters and the annotation with sorting option
• report/html/[0-9]/maps.html: summary of the cluster with expression profile, annotation,
and all sequences inside
• report/html/[0-9]/maps.fa: putative precursor
An example of the output is below: [image]
Easy start with bcbio-nextgen.py
Note:If you already are using bcbio, visit bcbio to run the pipeline there.
To install the small RNA data:
bcbio_nextgen.py upgrade -u development --tools --datatarget smallrna
Options to run in a cluster
It uses ipython-cluster-helper to send jobs to nodes in the cluster
• --parallel should set to ipython
• --scheduler should be set to sge,lsf,slurm
• --num-jobs indicates how much jobs to launch. It will run samples independently. If you
have 4 samples, and set this to 4, 4 jobs will be launch to the cluster
• --queue the queue to use
• --resources allows to set any special parameter for the cluster, such as, email in sge
system: M=my@email.com
Read complete usability here: https://github.com/roryk/ipython-cluster-helper An examples
in slurm system is:
--parallel ipython --scheduler slurm --num-jobs 4 --queue general
Output
• one folder for each analysys, and inside one per sample
• adapter: *clean.fastq is the file after adapter removal, *clean_trimmed.fastq is the
collapse clean.fastq, *fragments.fastq is file without adapter, *short.fastq is file
with reads < 16 nt.
• align: BAM file results from align trimmed.fastq
• mirbase: file with miRNA anotation and novel miRNA discovery with mirdeep2
• tRNA: analysis done with tdrmapper [citation needed]
• qc: *_fastqc.html is the fastqc results from the uncollapse fastq file
• seqcluster: is the result of running seqcluster. See its documentation for further
information.
• report/srna-report.Rmd: template to create a quick html report with exploration and
differential expression analysis. See example here
OUTPUTS
seqcluster
• counts.tsv: count matrix that can be input of downstream analyses. nloci will be 0
always that the meta-cluster has been resolved successfully. For instance, it can happen
that you got sequences you have a bunch of sequences mapping to hundreds of different
places on the genome, then seqcluster doesn’t resolve that, and put everything under the
larger region covered by those sequences. So, mainly, 0 all are good rows. The ann
column is just where the meta-clusters overlap with. It can happen that one name appears
many times if different locations of the meta-cluster map to different copies of that
feature. OR if the annotation file used had multiple lines for that.
• read_stats.tsv: number of reads for each sample after each step in the analysis. Meant
to give a hint if we lose a lot of information or not.
• size_counts.tsv: size distribution of the small RNA by annotation group. (position,
reads, cluster)
• seqcluster.json: json file containing all information. This file is used as the input of
the report suit.
• log/run.log: all messages at debug level
• log/trace.log: to keep trace of algorithm decisions
Report
Beside the static HTML report that you can get using report subcommand, you can download
this HTML. (watch the repository to get notifications of new releases.)
• Go inside seqclusterViz folder
• Open reader.html
• Upload the seqcluster.db file generated by report subcommand.
• Start browsing your data!
Meaning of different sections:
• Top-left table shows list of meta-clusters, user can filter by number ID or keywords.
• Top-right table shows positions where this meta-cluster has been detected.
• Expression profile along precursor: Lines are number of reads in that position of the
precursor. It is sum of the log2 RPM of the expression for each sample.
• Table: raw counts for each sample and sequence. Only top 100 are shown.
• secondary structure: The region with more sequences inside meta-cluster is used to plot
the secondary structure. Colors refers to abundance in each position. Darker means more
abundance.
An example of the HTML code: _ ..examples
EXAMPLES OF SMALL RNA ANALYSIS
miRQC data
About
mirRQC project
samples overview:
>> Universal Human miRNA reference RNA (Agilent Technologies, #750700), human brain total
RNA (Life Technologies, #AM6050), human liver total RNA (Life Technologies, #AM7960) and
MS2-phage RNA (Roche, #10165948001) were diluted to a platform-specific concentration. RNA
integrity and purity were evaluated using the Experion automated gel electrophoresis
system (Bio-Rad) and Nanodrop spectrophotometer. All RNA samples were of high quality
(miRQC A: RNA quality index (RQI, scale from 0 to 10) = 9.0; miRQC B: RQI = 8.7; human
liver RNA: RQI = 9.2) and high purity (data not shown). RNA was isolated from serum
prepared from three healthy donors using the miRNeasy mini kit (Qiagen) according to the
manufacturer's instructions, and RNA samples were pooled. Informed consent was obtained
from all donors (Ghent University Ethical Committee). Different kits for isolation of
serum RNA are available; addressing their impact was outside the scope of this work.
Synthetic miRNA templates for let-7a-5p, let-7b-5p, let-7c, let-7d-5p, miR-302a-3p,
miR-302b-3p, miR-302c-3p, miR-302d-3p, miR-133a and miR-10a-5p were synthesized by
Integrated DNA Technologies and 5′ phosphorylated. Synthetic let-7 and miR-302 miRNAs were
spiked into MS2-phage RNA and total human liver RNA, respectively, at 5 × 106 copies/μg
RNA. These samples do not contain endogenous miR-302 or let-7 miRNAs, which allowed
unbiased analysis of cross-reactivity between the individual miR-302 and let-7 miRNAs
measured by the platform and the different miR-302 and let-7 synthetic templates in a
complex RNA background. Synthetic miRNA templates for miR-10a-5p, let-7a-5p, miR-302a-3p
and miR-133a were spiked in human serum RNA at 6 × 103 copies per microliter of serum RNA
or at 5-times higher, 2-times higher, 2-times lower and 5-times lower concentrations,
respectively. All vendors received 10 μl of each serum RNA sample.
Commands
Data was download from GEO web with this script. The following 2 configs were used for the
two sets: mirqc samples and non mirqc samples. Samples were analyzed with bcbio with the
following commands
report
Report showing part of the output report of bcbio pipelines together with some validations
are here.
MIRNA ANNOTATION
miRNA annotation is running inside bcbio small RNAseq pipeline together with other tools
to do a complete small RNA analysis.
For some comparison with other tools go here.
You can run samples after processing the reads as shown below. Currently there are two
version: JAVA
Naming
See always up to date information here in mirtop open project.
It is a working process, but since 10-21-2015 isomiR naming has changed to:
• Nucleotide substitution: NUMBER|NUCLEOTIDE_ISOMIR|NUCLEOTIDE_REFERENCE means at the
position giving by the number the nucleotide in the sequence has substituted the
nucleotide in the reference. This, as well, is a post-transcriptional modification.
• Additions at 3' end: 0/NA means no modification. UPPER CASE LETTER means addition at the
end. Note these nucleotides don't match the precursor. So they are post-transcriptional
modification.
• Changes at 5' end: 0/NA means no modification. UPPER CASE LETTER means nucleotide
insertions (sequence starts before miRBase mature position). LOWWER CASE LETTER means
nucleotide deletions (sequence starts after miRBase mature position).
• Changes at 3' end: 0/NA means no modification. UPPER CASE LETTER means nucleotide
insertions (sequence ends after miRBase mature position). LOWWER CASE LETTER means
nucleotide deletions (sequence ends before miRBase mature position).
Processing of reads
REMOVE ADAPTER
I am currently using cutadapt.
cutadapt --adapter=$ADAPTER --minimum-length=8 --untrimmed-output=sample1_notfound.fastq -o sample1_clean.fastq -m 17 --overlap=8 sample1.fastq
COLLAPSE READS
To reduce computational time, I recommend to collapse sequences, also it would help to
apply filters based on abundances. Like removing sequences that appear only once.
seqcluster collapse -f sample1_clean.fastq -o collapse
Here I am only using sequences that had the adapter, meaning that for sure are small
fragments. The output will be named as sample1_clean_trimmed.fastq
Prepare databases
For human or mouse, follows this instruction to download easily miRBase files. In general
you only need hairpin.fa and miRNA.str from miRBase site. mirGeneDB is also supported,
download the needed files here.
Highly recommended to filter hairpin.fa to contain only the desired species.
miRNA/isomiR annotation with JAVA
MIRALIGNER
Download the tool from miraligner repository.
Download the mirbase files (hairpin and miRNA) from the ftp and save it to DB folder.
You can map the miRNAs with.
java -jar miraligner.jar -sub 1 -trim 3 -add 3 -s hsa -i sample1_clean_trimmed.fastq -db DB -o output_prefix
Cite
SeqBuster is a bioinformatic tool for the processing and analysis of small RNAs datasets,
reveals ubiquitous miRNA modifications in human embryonic cells. Pantano L, Estivill X,
Martí E. Nucleic Acids Res. 2010 Mar;38(5):e34. Epub 2009 Dec 11.
NOTE: Check comparison of multiple tools for miRNA annotation.
Convert to GFF3-srna
Use mirtop to convert to GFF3-srna format. This is the desired format to share the isomiR
information and can be used to join multiple projects together easily.
See to know how to convert all the output into a single file and share easily with
collaborators:
mirtop gff --format seqbuster --sps hsa --hairpin database/hairpin.fa --gtf database/hsa.gff3 -o test_out out_folder/*/*.mirna
Post-analysis with R
Use the outputs to do differential expression, clustering and descriptive analysis with
this package: isomiRs
To load the data you can use IsomirDataSeqFromFiles function and get the count data with
isoCounts to move to DESeq2 or similar packages.
Manual of miraligner(JAVA)
options
Add -freq if you have your fasta/fastq file with this format and you want a third column
with the frequency (normally value after x character):
>seq_1_x4
CACCGCTGTCGGGGAACCGCGCCAATTT
Add -pre if you want also sequences that map to the precursor but outside the mature miRNA
• Parameter -sub: mismatches allowed (0/1)
• Parameter -trim: nucleotides allowed for trimming (max 3)
• Parameter -add: nucleotides allowed for addition (max 3)
• Parameter -s: species (3 letter, human=>hsa)
• Parameter -i: fasta file
• Parameter -db: folder where miRBase files are(one copy at miraligner-1.0/DB folder)
• Parameter -o: prefix for the output files
• Parameter -freq: add frequency of the sequence to the output (just where input is fasta
file with name matching this patter: >seq_3_x67)
• Parameter -pre: add sequences mapping to precursors as well
input
A fasta/fastq file reads:
>seq
CACCGCTGTCGGGGAACCGCGCCAATTT
or tabular file with counts information:
CACCGCTGTCGGGGAACCGCGCCAATTT 45
output
Track file
*
.mirna.opt: information about the process
Non mapped sequences will be on
*
.nomap
Header of the
*
.mirna.out file:
• seq: sequence
• freq/name: depending on the input this column contains counts (tabular input file) or
name (fasta file)
• mir: miRNA name
• start: start of the sequence at the precursor
• end: end of the sequence at the precursor
• mism: nucleotide substitution position | nucleotide at sequence | nucleotide at
precursor
• addition: nucleotides at 3 end added:
precursor => cctgtggttagctggttgcatatcc
annotated miRNA => TGTGGTTAGCTGGTTGCATAT
sequence add: TT => TGTGGTTAGCTGGTTGCATATTT
• tr5: nucleotides at 5 end different from the annonated sequence in miRBase:
precursor => cctgtggttagctggttgcatatcc
annotated miRNA => TGTGGTTAGCTGGTTGCATAT
sequence tr5: CC => CCTGTGGTTAGCTGGTTGCATAT
sequence tr5: tg => TGGTTAGCTGGTTGCATAT
• tr3: nucleotides at 3 end different from the annotated sequence in miRBase:
precursor => cctgtggttagctggttgcatatcc
annotated miRNA => TGTGGTTAGCTGGTTGCATAT
sequence tr3: cc => TGTGGTTAGCTGGTTGCATATCC
sequence tr3: AT => TGTGGTTAGCTGGTTGCAT
• s5: offset nucleotides at the begining of the annotated miRNAs:
precursor => agcctgtggttagctggttgcatatcc
annotated miRNA => TGTGGTTAGCTGGTTGCATAT
s5 => AGCCTGTG
• s3:offset nucleotides at the ending of the annotated miRNAs:
precursor => cctgtggttagctggttgcatatccgc
annotated miRNA => TGTGGTTAGCTGGTTGCATAT
s3 => ATATCCGC
• type: mapped on precursor or miRNA sequences
• ambiguity: number of different detected precursors
Example:
seq miRNA start end mism tr5 tr3 add s5 s3 DB amb
TGGCTCAGTTCAGCAGGACC hsa-mir-24-2 50 67 0 qCC 0 0 0 0 precursor 1
ACTGCCCTAAGTGCTCCTTCTG hsa-miR-18a* 47 68 0 0 0 tG ATCTACTG CTGGCA miRNA 1
COLLAPSE FASTQ(.GZ) FILES
Definition
Normally quality values are lost in small RNA-seq pipelines due to collapsing after
adapter recognition. This option allow to collapse reads after adapter removal with
cutadapt or any other tool. This way the mapping can use quality values, allowing to map
using bwa for instance, or any other alignment tool that doesn't support FASTA files.
Methods
The new quality values are the average of each of the sequence collapse.
Example
seqcluster collapse -f sample_trimmed.fastq -o collapse
• -f is the fastq(.gz) file
• -o the folder where the outout will be created. A new FASTQ file, where the name stand
for:
@seq_[0-9]_x[0-9]
The number right after _x means the abundance of this sequence in the sample
HANDLING MULTI-MAPPED READS
Definition
multi-mapped reads are the sequences that map more than one time on the genome, for
instance, because there are multiple copies of a gene, like happens with tRNA precursors
Consequence
Many pipelines ignores these sequences as defaults, what means that you are losing at leas
20-30% of the data. In this case is difficult to decide where these sequences come from
and currently there are three strategies:
• ignore them
• count as many times as they appear: for instance, if a sequences map twice, just count
it two times in the two loci. This will due an over-representation of the loci
abundances, and actually is against the assumption of all packages that perform
differential expression in count data.
• weight them: divide the total count by the number of places it maps. In the previous
example, each loci would get 1/2 * count. This produces weird dispersion values for
packages that fit this value as part of the model.
Our implementation [image]
We try to decide the origin of these sequences. The most common scenario is that a group
of sequences map two three different regions, probably due to multi-copies on the genome
of the precursor.
We introduce two options:
• most-voting strategy: In this case, we just count once all sequences, and we output this
like one unit of transcription with multiple regions. This is the option by default.
• bayes inference: we give the same prior probability to all locations, and use the number
of sequences starting in the same position than the one we are trying to predict its
location as P(B|A). With this we calculate the posterior that will be used to get the
proportion of counts to the different locations. We apply the code from the book: "Think
Bayes" ( Allen B. Downey). This is still under development. To activate this option, the
user just needs to add --method babes
The main advantage of this, it is that it can be the input of any downstream analysis that
is applied to RNA-seq, like DESeq, edgeR ... As well, there is less noise, because there
is only one output coming from here, not three.
TOOLS FOR DOWNSTREAM ANALYSIS
Web-servers
TFmiR: disease-specific miRNA/transcription factor co-regulatory networks v1.2. It uses
results from UP/DOWN regulated miRNA/Genes and allows to focus in only one disease to
create different type of relationships between miRNA/TF/Gene. Easy to use. Probably need
to filter the output sometime due to the big networks that can result from an analysis.
Diana-TarBase v7.0: Database for validated miRNA targets. Many filter options. Good for
small candidate miRNAs set studies.
StarScan: Database to browse the targets of miRNAs from degradome data. It has a fancy
interface, and many species and data from GEO.
miRtex gives targets from literature. Good for finding validated targets to help
discussion in papers or further functional experiment based on new hypothesis.
piRBase: Database for piRNA annotation and function. Published last year, for now the best
I can find out there.
chimira: Web tool to analyze isomiR. It gives you a quick idea of you samples.
MicroCosm: MiRNA target database. Updated and download option.
IsomiR Bank: isomiR database from many species and tissues. For single queries is useful.
Command-lines
miRVaS : tools to predict the functional changed due to nt changes in the miRNA sequence.
RELEVANT PAPERS ABOUT ISOMIRS AND OTHER NOVEL SMALL RNAS WITH FUNCTIONAL RELEVANCE
Validation
• Our approach can be adapted to many polyadenylation-based RT-qPCR technologies already
exiting, providing a convenient way to distinguish long and short 3′-isomiRs.
IsomiRs
Naturally existing isoforms of miR-222 have distinct functions: this work demonstrates the
capacity for 3' isomiRs to mediate differential functions, we contend more attention needs
to be given to 3' variance given the prevalence of this class of isomiR.
miR-142-3p isomiR: "We furthermore demonstrate that miRNA 5′-end variation leads to
differential targeting and can thus broaden the target range of miRNAs."
A highly expressed miR-101 isomiR is a functional silencing small RNA.
A challenge for miRNA: multiple isomiRs in miRNAomics.
miR-183-5p isomiR changes in breast cancer. Validated target regulation of new genes
different from the reference miRNA.
A comprehensive survey of 3' animal miRNA modification events and a possible role for 3'
adenylation in modulating miRNA targeting effectiveness.
PAPD5-mediated 3′ adenylation and subsequent degradation of miR-21 is disrupted in
proliferative disease.
High-resolution analysis of the human retina miRNome reveals isomiR variations and novel
microRNAs.
Sequence features of Drosha and Dicer cleavage sites affect the complexity of isomiRs.
Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully
discriminate amongst 32 TCGA cancer types
General
A novel piRNA mechanism in regulating gene expression in highly differentiated somatic
cells.
Differential and coherent processing patterns from small RNAs to detect changes in
profiles of processing small RNAs.
Survey of 800+ datasets from human tissue and body fluid reveals XenomiRs are likely
artifacts
Targets
Identification of factors involved in target RNA-directed microRNA degradation.
Techonolgy
miRQC: work studying the accuracy and specificity of different technologies to detect
miRNAs.
Important features affecting the detection of small RNA biomarkers: How the sample can
affect the detection of biomarkers (like RIN value, concentration, ...)
Comparison of alignment and normalization . I will take the message that TMM and DESeq/2
normalization are the best to avoid strong bias if we consider to have a small proportion
of DE miRNAs. For the alignments, here you have another comparison for miRNAs annotation:
https://rawgit.com/lpantano/tools-mixer/master/mirna/mirannotation/stats.html
review of tools for detect miRNA-disease network.
review of tools for miRNA de-novo and interaction analysis
Evaluation of microRNA alignment techniques BIG meeting on Dec,3 2015:
bcbio-srnaseq-BIG-20151203.pdf
DOCUMENTATION
CLASSES
Visit GitHub code
I am in the process to document all classes and methods
• Index
• Module Index
• Search Page
AUTHOR
Lorena Pantano, Francisco Pantano, Eulalia Marti
COPYRIGHT
2022, Lorena Pantano