Provided by: seqcluster_1.2.9+ds-3_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
2023, Lorena Pantano
1.2 Jan 21, 2023 SEQCLUSTER(1)