Standards, Guidelines and Best Practices for RNA-Seq: 2010/2011
I. Introduction: Sequence based assays of transcriptomes (RNA-seq) are in wide use
because of their favorable properties for quantification, transcript discovery and spliceisoform identification, as well as adaptability for numerous more specializedmeasurements. RNA-Seq studies present some challenges that are shared with priormethods such as microarrays and SAGE tagging, and they also present new ones thatare specific to high-throughput sequencing platforms and the data they produce.
RNA-Seq is not a mature technology. It is undergoing rapid evolution of biochemistry ofsample preparation; of sequencing platforms; of computational pipelines; and ofsubsequent analysis methods that include statistical treatments and transcript modelbuilding. This document is part of an ongoing effort to provide the community withstandards and guidelines that will be updated as RNA-Seq matures and to highlightunmet challenges. The intent is to revise this document annually to capture newadvances and increasingly consolidate standards and best practices.
RNA-Seq experiments are diverse in their aims and design goals, currently including
multiple types of RNA isolated from whole cells or from specific sub-cellularcompartments or biochemical classes, such as total polyA+ RNA, polysomal RNA,
nuclear ribosome-depleted RNA, various size fractions of RNA and a host of others. The
goals of individual experiments range from major transcriptome “discovery” that seeks todefine and quantify all RNA species in a starting RNA sample to experiments that simply
need to detect significant changes in the more abundant RNA classes across many
samples for the purpose of cell phenotyping. The guidelines and standards discussedhere do not exhaustively cover the entire matrix of this experimental space, but insteademphasize best practices designed to support “reference quality” transcriptome
measurements for major RNA sample types.
Different study aims and RNA types will therefore call for appropriate adjustments instandards developed for reference measurements. However, other parts of the standards
recommended, such as providing proper meta-data to describe the sample andprocessing should be widely applicable.
II. Information to be supplied with each sample used for an RNA-seq experiment.
To be useful to the scientific community, RNA-seq data should be accompanied byinformation concerning the biological source of the RNA and protocols used to extract
and prepare the RNAs.
1. For cell lines the following information should be recorded and provided:a) Cell line source and lot number.
b) Growth time/passage number.
c) Cell density.
d) Cite protocol used to culture cell lines.
e) Cite results of tissue culture contaminant (e.g.mycoplasma/ wolbacia) tests
if conductedf) Confirmation of freezing cell aliquots of examined lines
2. For sub-cellular compartments, tissues, organs or whole organisms, thefollowing should be recorded and provided:a) animal strain and genotype.

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12011 Mayb) individual genome sequence source data, where availablec) Protocols tissue or cell type preparation, such cell sorting markers etc,
embryo staging, etc. Estimates of purity from cellular enrichments should beprovided.
d) Amounts of starting material (tissue/organ weights, cell number from which
sub-cellular compartments were isolated, etc).
e) Estimate of enrichment or homogeneity of sample from other associatedbiological elements (e.g. degree of nuclear enrichment compared toassociated cytosolic elements, percent homogeneity of CD8+ cells, fraction ofanimals of the stated stage).
3. Identification of the type of RNA targeted (size range, poly A+ or A-, 5’ capped or
uncapped, polysomal, etc).
4. Protocols used to isolate RNAs (size range, 5’/5’-3’tags, poly A+/A-). While each
RNA type has unique issues associated with the intended purification, evidenceof the enriched status of the targeted RNA type should be recorded. This could
include length profiles of the isolated RNAs, the amount of ribosomal RNA
present in poly A+ samples or evidence of 5’ cap modifications.
5. Methods used to quantify RNAs prior to sequencing: An appropriately sensitiveand precise measurement of RNA input is critical. Current implementations ofwidely used flourimetric or uv spectrophotometric methods adapted for small
sample inputs offer mutually supporting complementary data, and agreeing
measurements by two different methods are advised. A few applications do notpermit such measures (i.e. single cell RNA-Seq).
III.

RNA Sequence Experiment Design: Replication and sequencing depth
1. Replicate number: Experiments should be performed with two or morebiological replicates, unless there is a compelling reason why this is impracticalor wasteful (e.g. overlapping time points with high temporal resolution). A
biological replicate is defined as an independent growth of cells/tissue andsubsequent analysis. Technical replicates from the same RNA library are notrequired, except to evaluate cases where biological variability is abnormally high.
In such instances, separating technical and biological variation is critical. In
general, detecting and quantifying low prevalence RNAs is inherently morevariable than high abundance RNAs. A typical R2 (Pearson) correlation of geneexpression (RPKM) between two biological replicates, for RNAs that are detectedin both samples using RPKM or read counts, should be between 0.92 to 0.98.
Experiments with biological correlations that fall below 0.9 should be either berepeated or explained.
2. Sequencing depth. The amount of sequencing needed for a given sample isdetermined by the goals of the experiment and the nature of the RNA sample.
Experiments whose purpose is to evaluate the similarity between thetranscriptional profiles of two polyA+ samples may require only modest depths ofsequencing (e.g. 30M pair-end reads of length > 30NT, of which 20-25M aremappable to the genome or known transcriptome, Experiments whose purpose isdiscovery of novel transcribed elements and strong quantification of known
transcript isoforms requires more extensive sequencing. The ability to detect

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reliably low copy number transcripts/isoforms depends upon the depth ofsequencing and on a sufficiently complex library. For experiments from a typicalmammalian tissue or in which sensitivity of detection is important, a minimum
depth of 100-200 M 2 x 76 bp or longer reads is currently recommended.
[Specialized studies in which the prevalence of different RNAs has been
intentionally altered (e.g. “normalizing” using DSN) as part of sample preparationneed more than the read amounts (>30M paired end reads) used for simple
comparison (see above). Reasons for this include: (1) overamplification of inserts
as a result of an additional round of PCR after DSN and (2) much more broad
coverage given the nature of A(-) and low abundance transcripts.
IV. Information to report: Sample preparation metadata
1. Method of Preparation of cDNA. The method of preparing cDNAs made from thetargeted RNAs for sequencing must be described in the metadata supplied withsequencing data. The specific of cDNA priming (oligodT or random) and thelength of RNA at the time of cDNA copying when random priming is used affectthe outcome significantly and should be reported. Information concerning thecDNA preparation should include information concerning all details involved in
making cDNAs for sequencing, including the use of bar-codes for multiplex
sequencing.
2. Quantitative standards (spike-ins). It is highly desirable to include a ladder ofRNA spike-ins to calibrate quantification, sensitivity, coverage and linearity.
Information about the spikes should include the stage of sample preparation thatthe spiked controls were added, as the point of entry affects use of spike data inthe output. In general, introducing spike-ins as early in the process as possible isthe goal, with more elaborate uses of different spikes at different steps being
optional (e.g. before poly A+ selection, at the time of cDNA synthesis, or justprior to sequencing). Different spike-in controls are needed for each of the RNA
types being analyzed (e.g. long RNAs require different quantitative controls fromshort RNAs). Such standards are not yet available for all RNA types. Information
about quantified standards should also include:a) how many individual spike-ins usedb) source of the spike-ins (home-made or commercial or NIST)c) amount added for each individual spike-ind) spike sequences for primary read data use by otherse) the concentration of each of the spike-ins in the pool used.
3.

cDNA and sequencing design: This information should indicate whether themethod allowed for the generation of strand specific or unstranded data, whether
the sample consists of pooled and bar coded RNA targets, sequencing platform
used, depth of sequencing (e.g. number of reads obtained), length of sequencereads, whether the reads are in single or paired-end format.

V. Information to report: Post-sequencing mapping, read statistics, quality scores
1. Mapping of sequence data: Multiple short read mapping algorithms are
currently available to map reads to genome assemblies and to transcript modelcollections. Because they have not as yet been subjected to systematiccomparisons (though this is planned), data producers should select the program
they feel provides the best results. Information specifying the mapping needs toRNA standards v1.03

2011 Maybe provided in sufficient detail to be reproduced, including: the program andversion used, parameters employed, etc. In addition, information concerning thesequence version of the reference genome and, as appropriate, collections oftranscript model sequences or splice junction collections (i.e. Refseq,Genecode, UCSC). Treatment of reads mapping equally well to more than one
site in the reference genome or model set needs to be specified.
2. Specifying thresholds used:a) number of allowed mis-matches, minimal score, etc.
b) treatment of multiple mapping reads: Was there a cap on number of loci towhich multiple reads were distributed (e.g. only loci with <10 reads arereported). Specify the algorithm by which multi-mapping reads weredistributed.
c) quality scores used to filter the readsd) paramters used to trim reads (e.g. those based on quality scores andpresence of linkers)e) for “split reads”, whether there are constraints regarding the location of thesplits (i.e. within the same chromosome; within a certain genomic interval)
and regarding the sequences at the split (allowed only at canonicaljunctions, etc.)
3. Information concerning mapping strategy. Specify if mapping was performed
relative to the genome and/or transriptome or a combination, and if so, informationon the version of the genome used, and the transcriptome of reference (RefSeq,ENSEMBL, GENCODE, etc). Specify the order of steps in the mapping pipeline:
simultaneous genome and transcriptome mapping, or stepwise mapping, andwhether novel annotation derived from the data itself is part of the final mapping.
4. Information concerning mapped results: Several baseline statistics should beprovided for each sample. Reporting 4a-4d below are expected, while 4e-4g arerecommended when applicable.
a) total number of uniquely mapped reads (i.e. occurs once in referencegenome)b) if paired-end reads are used, report the number of mapped read pairs(involving the mated pair or each read) and the number of mapped single reads.
c) When appropriate, provide quantitation of various mapped elements
including exons, splice sites, CAGE and PET tags, transcripts. Publishedapproaches of normalization (RPKM/FPKM) are among those that might
be applied.
d) Reproducibility of replicates. Evaluation of reproducibility for theexistence and quantification of different RNA types (e.g. long vs. shortRNAs, CAGE vs., splice sites) currently require treatments suited to thespecific data-type and its analysis. Establishing the best practices foreach is an active area of research. Algorithmic approaches such as IDR(http://www.encodestatistics.org/publications/IDR101.pdf) can be applied
if determined appropriate.
The specific implementation and settings
used for any of these algorithms should be explicitly reported and madeavailable. For IDR the result should be used as a metric of reproducibility
and not as a metric to derive a false discovery rate. Alternatively,RNA standards v1.04

2011 Maycorrelation metrics as a function of prevalence can be used. This may
prove more sensitive and be more appropriate. For messenger RNA, atarget is that biological replicates display > 0.9 correlation for transcripts/features greater than 1 RPKM in two or more replicates.
e) Estimating depth of coverage for mRNA-Seq. A routine approximation of
the sequence coverage achieved for an mRNA-seq experiment can bemade from mappable reads (unique and multiple mappers). To estimate
the sequence coverage per mRNA of an average length (ignoring that
there is actually a broad length distribution) present at 1 copy per cellbased on an estimated input of the number of mRNAs the following
calculation can be used: (Total sequence NT in the sequencing reaction /Estimate of the Number of Molecules of mRNA/cell ) / (1,500NT/mRNA).
Example: 10^10 nucleotides sequenced / 2X10^6 mRNAs/cell =5X10^3NT sequence coverage per/mRNA.
5X 10^3 NT /1.5 X^3NT/mRNA ~3X sequence coverage of an RNA present at one copy percell. More prevalent RNAs have proportionately higher coverage. This is,of course, highly sensitive to the number picked for mRNAs/cell, and thisnumber is poorly known for most systems. In addition, in the cases oftissues/organs, whole animals acting as the source of the RNA, anestimate of the number of cells is difficult.
f)Empiric coverage. This evaluation can be performed informatively for
spike-in standards and/or for the top ~ 30% of annotated RNA in theprevalence spectrum. While uniform sequence coverage over eachtranscript is a goal in RNA-Seq, it is widely appreciated that this is not
fully achieved using the current dominant technologies.
Variables
affecting uniformity include the method of cDNA synthesis, secondary
structure of individual transcripts, length of the RNA template at the pointof cDNA priming, method of priming, and GC content. Evaluating overall
coverage (inclusion) and quantitative uniformity of coverage are further
complicated for genes with multiple possible transcript isoforms than forgenes with a single isoform.
It is therefore recommended thatassessments of coverage be performed for sub-sets of transcripts having
single isoforms. Spike-in standards, in this context, offer the advantage
of presenting a single known isoform.
A simple average empiric
coverage calculation can use the total sequence mapped for spike-ins, orfor an appropriately selected endogenous transcript set, divided by theknown calculated sequence length of that RNA (i.e. its sequence
complexity). Coverage is expected to be a function of the prevalence ofthe transcript in the sample and of depth of sequencing.

g) The 3'-5' coverage ratio can be used as a metric in mRNA-seq. This ishighly relevant to polyA selected templates and/or success of oligo-dT
priming, but it is not appropriate for many other transcriptome fractions
and sample preparation protocols. In theory, one would like this ratio toapproach 1.0 for mRNA, but the existence of multiple transcript isoforms
with differing 3’ and 5’ exons can complicate this expectation, as willtechnical issues with end-representation (see below). A 3’/5’ ratio istherefore best applied to a sub-set of test loci having unique 3’ and 5’

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exons, and also to spike-in RNAs that are polyadenylated and added prior
to any oligo dT selection step. If the 3'/5' ratio is high for such mRNAs or
spike-in standards, it suggests problems with degraded RNA input or with
cDNA synthesis biases.
Given over- and under-representation ofsequence coverage of the 5’ and 3’ 100-200 nucleotides of mRNA duringhigh throughput sequencing (Hillier, et al 2009 Genome Res 19:657) thecoverage ratio should be evaluated outside these end effects.
5. Estimating technical and mapping error: Matching millions of short and often
error-prone reads against 100’s of millions or billions of bases of genomic sequence
generates mapping errors. Particularly problematic sources of mapping error in
RNA-Seq include sequence reads from multigene families and paralogous genes,
when some members are highly expressed in the sample, since these will generatemany instances of reads that vary slightly from each other, and whose mapping with tolerated mismatches - will therefore be in error. The mis-mapping frequency
for these is expected to be a complex function of read length and format (paired-end
or single reads), sequencing platform biases, mismatch threshold, and expressionlevels of paralogous genes represented in a transcriptome.
In principle, one could address the problem of a null model for the majority of thetranscriptome that is nonparalogous by using known non-transcribed portions of
the genome to derive a null model to estimate the extent of mapping error.
However, the biology of transcription in most organisms appears to include
some transcription of much of the genome, making it difficult to implement thisapproach with confidence.
In addition, genomic or organelle DNA
contamination of an RNA prep can thwart interpretation of read mapping errorby this approach, when reads from DNA are wrongly attributed to mis-mapped
transcript reads. This is a current research challenge, motivating estimates ofsequencing error and mis-mapping by simulation and by other methods, that
can then inform and help to adjust thresholds of detection at transcript andsplice junction levels. It is therefore important to report, in sufficient detail toallow reproduction by others, the nature of any null model and/or the derivationof thresholds by the methods used on a study-by-study basis.
6. Analysis of spike-in standards: Using the reads that map to the spike-in standards
it is possible to determine:a) Individual spike-in detectionb) Percent of spike-in sequence detected (sequence coverage)c) Correlation of spike-in data with its dosed amountd) Quantification of antisense sequences to spike-ins for strand specificmethods to assess the levels of inappropriate antisense signale) For paired format data, quantify erroneous mate-pair frequency inwhich one read maps to one spike-in and the other read maps toeither another spike-in or to the genome to determine a rate of strandswitching. This should particularly be assessed in experiments aimed
at discovering chimeric or trans-spliced transcripts.
f) It is recommended that the average coverage be greater than 1x for
spike-in transcripts that are > 1 x 10-6 in the sample.

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VI Novel Elements.
When appropriate to the study, it is desirable to report information concerning
novel transcribed elements (minimally defined as clusters of reads that are not connected toany annotated transcript) and their estimated transcript abundances. Some currently
available programs assemble and report these as simple read clusters, while others produce
detailed models of novel transcript elements from RNA-Seq input. Depending on thesoftware and appropriateness of the data, quantification at the level of individual transcript
isoform models is offered or quantification can be at the level of read clusters. The program,version, parameters and, if appropriate, transcriptome annotation and model set usedshould be reported in a standard GFF format, with transcripts that are compatible with
explicitly compatible with curated/reference annotations labeled with the corresponding
annotation transcript ID.
While not required for the submission of raw read mappings, care should betaken to explicitly define the level of “novelty” that is claimed in the analysis of the data andthe level of compatibility with the existing, curated annotations. For example, a transcript
might be “novel” when compared to Refseq yet already be in GENCODE for human in
polyA+ samples corresponding to mRNA. Furthermore, novel transcript isoforms of known
genes should be sub-categorized as: (a) 5’ or 3’ extensions of known transcripts, (b) novel
splice isoforms [and whether they change the predicted protein-coding sequence], (c)whether they encode an ORF or known subtype of non-coding RNA (snoRNA, etc…), (d)whether they could be the result of incomplete splicing from nuclear RNA, and (e) whether
they are related to repeat elements (whether overlapping or adjoining to ribosomal RNA
repeats, for example). Elements or transcripts containing non-canonical splice junctions
should be explicitly flagged as such. Novel transcripts that are anti-sense to known models
in RNA-seq samples from strand-preserving protocols should be filtered for low-level strandmismapping contamination (which can be measured from the spike-ins). When replicatesare available, the novel annotation should be present in both replicates.

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