mrin for direct assessment of genome-wide and gene-specific mrna integrity from large-scale RNA-sequencing data

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1 Received 7 My 215 Accepted 15 Jun 215 Published 3 Aug 215 for direct ssessment of genome-wide nd gene-specific mrna integrity from lrge-scle RNA-sequencing dt Huijun Feng 1,2, Xuegong hng 1 & Cholin hng 2 DOI: 1.138/ncomms8816 OPEN The volume of RNA-Seq dt sets in public repositories hs been expnding exponentilly, providing unprecedented opportunities to study gene expression regultion. Becuse degrded RNA smples, such s those collected from post-mortem tissues, cn result in distinct expression profiles with potentil bises, prticulrly importnt step in mining these dt is qulity control. Here we develop method nmed to directly ssess mrna integrity from RNA-Seq dt t the smple nd individul gene level. We systemticlly nlyse lrge-scle RNA-Seq dt sets of the humn brin trnscriptome generted by different consorti. Our nlysis demonstrtes tht 3 bis resulting from prtil RNA frgmenttion in post-mortem tissues hs mrked impct on globl expression profiles, nd tht effectively identifies smples with different levels of mrna degrdtion. Unexpectedly, this process hs reproducible nd gene-specific component, nd trnscripts with different stbilities re ssocited with distinct functions nd structurl fetures reminiscent of mrna decy in living cells. 1 MOE Key Lbortory of Bioinformtics nd Bioinformtics Division, TNLIST/Deprtment of Automtion, Tsinghu University, Beijing 184, Chin. 2 Deprtment of Systems Biology, Deprtment of Biochemistry nd Moleculr Biophysics, Center for Motor Neuron Biology nd Disese, Columbi University, New York, New York 132, USA. Correspondence nd requests for mterils should be ddressed to C.. (emil: cz2294@columbi.edu). NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

2 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 mrna sequencing (RNA-Seq) provides digitl profiling of gene expression with unprecedented depth, resolution nd coverge 1,2. To obtin relible nd reproducible RNA-Seq dt, RNA qulity is of prmount importnce 3. One chllenge to obtining high-qulity RNA is RNA degrdtion before nd during smple collection in preprtion for RNA isoltion due to tissue necrosis, which is n issue especilly for smples collected in clinicl settings nd field studies 4,5. For exmple, humn trnscriptome studies, such s the BrinSpn ( nd Genotype-Tissue Expression (GTEx) 6 projects, rely on post-mortem tissues. In ddition, it is well documented tht post-mortem intervls nd cell stressors such s hypoxi cn hve substntil impct on RNA integrity 7,8. A widely dopted mesure of RNA integrity is n electrophoretic-bsed method to derive n RNA integrity number () 9,1. This method uses mchine-lerning pproch to extrct fetures from electrophoretic trces (Bionlyzer) nd trin neurl network tht is predictive of s in lrge set of smples mnully curted by experts. In the context of RNA-Seq dt nlysis, severl recent studies investigted how degrded RNA smples of different s cn ffect detection of differentilly expressed genes 4,11. By introducing experimentlly controlled RNA degrdtion either before or fter RNA extrction, these studies demonstrted tht s relibly reflected the severity of degrdtion nd tht the use of degrded RNA smples could result in thousnds of flse positives, while the genuine chnges could be msked if corrective mesures were not implemented. One pprent consequence of degrded RNA on RNA-Seq is the 3 bis of red coverge long messenger RNA (mrna) trnscripts. Severl qulity control (QC) metrics hve recently been developed to detect such bis, flg problemtic smples nd identify other technicl issues 12,13. However, to our knowledge, no systemtic studies hve crefully chrcterized RNA degrdtion in post-mortem smples nd their impct on gene expression quntifiction with wide rnge of tissue types cross hundreds of smples. Such n nlysis is urged to develop the optiml prctices in mining the rich informtion provided by lrge-scle trnscriptome sequencing projects nd by metnlysis of enormous dt generted by individul studies vilble in public repositories 14. Severl chllenges re present in QC of RNA-Seq dt derived from post-mortem tissues vilble in the public domin. First, there is currently no consensus with regrd to the required to generte high-qulity dt, nd the criteri vry substntilly depending on the specific studies used. Thus it is not unusul tht re-nlysis or met-nlysis of published dt sets prefers more stringent filtering thn the originl studies tht generted the dt sets. Second, in public repositories, s of RNA smples used to generte RNA-Seq librries re frequently not reported nd thus re-nlysis in follow-up studies hs to perform QC on the RNA-Seq dt directly. Third, the use of s s mesure of RNA-Seq dt qulity cn hve potentil cvets. In prticulr, while is derived lrgely bsed on the integrity of ribosoml RNAs (rrnas) 9, the decy process of mrna trnscripts might be distinct nd even gene specific, which is not necessrily reflected in the completely. To ddress these chllenges, here we develop method to estimte mrna integrity number () from RNA-Seq dt sets directly bsed on quntittive modelling of the 3 bis of red coverge. We pplied this method to the lrge-scle RNA-Seq dt sets generted by the BrinSpn nd GTEx projects to demonstrte its effectiveness in quntifying mrna degrdtion tht mrkedly ffected the globl expression profiles. Unexpectedly, our method lso suggests tht the degree of degrdtion vries mong different genes, nd substntil frction of the vrition cn be explined by functionl nd structurl fetures of the ssocited trnscripts. Results 3 bis in degrded RNA hs globl impct on gene expression. The mechnism of RNA degrdtion in post-mortem smples during tissue necrosis is not well understood. The 3 bis observed in RNA-Seq dt could rise from RNA degrdtion by 5 exonucleses 15,16. Alterntively, RNA frgmenttion by endonucleses or stochstic hydrolysis cn lso introduce the sme pprent bis fter poly-dt selection to purify polydenylted mrna, which is currently stndrd step in minstrem RNA-Seq librry preprtion protocols including the Illumin TruSeq RNA Smple Prep Kit. To demonstrte the importnce of ssessing mrna integrity in RNA-Seq dt derived from post-mortem tissue smples nd to distinguish different types of mrna degrdtion, we first used lrge dt set generted by the BrinSpn project. The dtset used in this nlysis is composed of gene nd exon quntifictions of 578 smples derived from multiple brin structures of postmortem humn brins t different developmentl stges (Supplementry Dt 1). Importntly, Affymetrix HuEx exon microrrys were used s n independent pltform to profile lrgely overlpping set of smples (479 smples profiled by both pltforms) 17. While poly-dt selection ws used to deplete rrnas in RNA-Seq librry preprtion, it ws not used to prepre complementry DNAs (cdnas) for HuEx exon microrry hybridiztion 18, key difference of the two pltforms. Therefore, prtil mrna frgmenttion will lest likely result in significnt 3 bis in HuEx probe signls long trnscripts. We begn with hierrchicl clustering nlysis of gene expression profiles 19 s mesured by RNA-Seq nd microrrys, respectively, to obtin globl view of the developing brin trnscriptome. For both dt sets, clustering of smples lmost perfectly seprted fetl brins from neontl nd dult brins, which is consistent with the mrked gene expression chnges during development (Fig. 1). However, there re importnt differences between the two dt sets. In the RNA-Seq dt set, subset of smples show very distinct expression profiles chrcterized by low levels of expression in vst mjority but not ll genes. Most of these smples re postntl; some, however, re fetl. More creful exmintion shows tht smples derived from the sme individuls, regrdless of brin regions or ge, tightly clustered together (Fig. 1,b). This vst, chrcteristic under-representtion of gene expression ws not observed in the sme smples mesured by microrrys (see lso below). One possible cuse of the observed skew in the RNA-Seq dt is tht RNA-Seq is more sensitive to RNA qulity thn microrrys re (in prticulr, prtil frgmenttion of RNA), due to the poly-dt selection step. This cn lso explin why postntl brins were ffected more severely thn fetl brins, becuse for the ltter, high-qulity tissues re more ccessible. To test this hypothesis, we first exmined individul genes in smples with presumed RNA degrdtion. One exmple is the Smg1 gene in smples derived from individul VIII_5, 4-yer-old mle, which were profiled by both RNA-Seq nd exon microrrys (Fig. 1c). While the microrry dt show reltively uniform probe intensities from different exons throughout the trnscript, the RNA-Seq dt suffer from severe 3 bis nd the reds re predominntly limited to the lst exon. The low red coverge throughout the trnscript, except the very 3 end, hs likely cused the underestimtion of Smg1 expression. These observtions suggest tht prtil mrna frgmenttion is likely mjor source of bis in gene expression quntifiction of degrded post-mortem brin smples. 2 NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

3 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 ARTICLE BrinSpn RNA-Seq BrinSpn HuEx Fetl (233) Postntl (345) Fetl (254) Postntl (238) c Smg1 hg19 5 kb 18,93, 18,92, 18,91, 18,9, 18,89, 18,88, 18,87, 18,86, 18,85, 18,84, 18,83, RefSeq Genes hs_br Coverge Grph RNAseq (Brwnd) H376_VIII_5_4yrs_M_DFC Coverge Grph H376_VIII_5_4yrs_M_VFC Coverge Grph H376_VIII_5_4yrs_M_MFC Coverge Grph BrinSpn RNA-Seq H376_VIII_5_4yrs_M_M1C Coverge Grph H376_VIII_5_4yrs_M_S1C Coverge Grph H376_VIII_5_4yrs_M_IPC Coverge Grph H376_VIII_5_4yrs_M_A1C Coverge Grph H376_VIII_5_4yrs_M_STC Coverge Grph H376_VIII_5_4yrs_M_ITC Coverge Grph H376_VIII_5_4yrs_M_V1C Coverge Grph H376_VIII_5_4yrs_M_DFC Coverge Grph H376_VIII_5_4yrs_M_VFC Coverge Grph Reltive expression H376_VIII_5_4yrs_M_MFC Coverge Grph H376_VIII_5_4yrs_M_M1C Coverge Grph b 1: IV_53 (19 pcw, F) 2: VII_5 (6mos, F) 3 3: VIII_53 (2yrs, F) 3 BrinSpn HuEx H376_VIII_5_4yrs_M_S1C Coverge Grph H376_VIII_5_4yrs_M_IPC Coverge Grph H376_VIII_5_4yrs_M_A1C Coverge Grph H376_VIII_5_4yrs_M_STC Coverge Grph A1C STC IPC M1C_S1C V1C HIP AMY ITC IPC A1C DFC OFC S1C M1C STC MFC VFC V1C MD STR HIP MD AMY ITC IPC A1C M1C DFC VFC V1C S1C STC MFC OFC STR H376_VIII_5_4yrs_M_ITC Coverge Grph H376_VIII_5_4yrs_M_V1C Coverge Grph Figure 1 Impct of presumptive mrna degrdtion on globl gene expression profiling. () Gene expression profiles of post-mortem brin smples in the BrinSpn dt s mesured by RNA-Seq (left) nd Affymetrix HuEx exon microrrys (right). Genes nd smples re ordered by centroid linkge hierrchicl clustering of ech dt set. In the RNA-Seq dt, subset of smples, frequently collected from the sme individuls, show pervsive under-representtion of gene expression (exmples lbelled with 1, 2 nd 3 below the het mp). (b) Exmples of fetl (1) nd postntl (2 nd 3) brin smples derived from the sme individuls for which the globl gene expression profiles pper to be severely ffected by RNA degrdtion. (c) Smg1 s n exmple of discrepncy in RNA-Seq nd exon rry dt with respect to 3 bis. RNA-Seq nd exon rry dt shown re from different brin structures of the sme individul (VIII_5); 14/16 smples from this individul re rnked mong the 5 most degrded smples. For comprison, seprte trck shows brin RNA-Seq dt derived from n independent study 47. A1C, primry uditory cortex (core); AMY, mygdloid complex; DFC, dorsolterl prefrontl cortex; HIP, hippocmpus (hippocmpl formtion); IPC, posteroventrl (inferior) prietl cortex; ITC, inferolterl temporl cortex (re TEv, re 2); M1C, primry motor cortex (re M1, re 4); MD, mediodorsl nucleus of thlmus; MFC, nterior (rostrl) cingulte (medil prefrontl) cortex; OFC, orbitl frontl cortex; S1C, primry somtosensory cortex (re S1, res 3,1 nd 2); STC, posterior (cudl) superior temporl cortex (re TAc); STR, stritum; V1C, primry visul cortex (strite cortex, re V1/17); VFC, ventrolterl prefrontl cortex. quntifies 3 bis nd ltertion in gene expression. To estimte mrna degrdtion directly nd quntittively from RNA-Seq dt, we went forwrd to develop sttisticl method to quntify the 3 bis of ech gene, from which single-number is derived for ech smple (Fig. 2; Methods). Specificlly, for ech gene nd smple, mpped RNA-Seq reds re counted to obtin red coverge profile cross ll exonic positions of representtive RefSeq trnscript (step 1), which is converted into cumultive coverge profile (step 2). This cumultive profile is then compred with null distribution one would expect if reds re uniformly distributed. Chnge of the cumultive distribution due to 3 bis is quntified by modified Kolmogorov Smirnov (KS) sttistic 2. To further mitigte dditionl bises tht cnnot be explined by the 3 bis due to mrna degrdtion (for exmple, sequencing rtefcts due to locl bse compositions 21, inccurcy of trnscript nnottion or lterntive RNA processing), we normlize the mtrix of KS sttistics by subtrcting the medin cross smples (mks mtrix, step 3). Finlly, the of ech smple is defined s the negtive of the verge mks vlues cross ll genes in the smple. With this mesure, smples with negligible degrdtion re expected to hve s following pproximtely norml distribution with zero men, while degrded smples hve negtive s with lrger devitions from which the sttisticl significnce of degrdtion cn be evluted (steps 4 nd 5; Supplementry Fig. 1). In the nlysis of the BrinSpn RNA-Seq dt, modifiction we dopted to obtin the mks mtrix nd s of smples is tht red coverge profiles were estimted t the exon insted of the nucleotide resolution due to the vilbility of dt (Fig. 1c); however, we expect this to hve very moderte effects on (see below). As expected, the distribution of s hs hevier til on the left deviting from norml distribution, indicting degrded RNA smples. In totl, 141 (24%) nd 17 (29%) smples were clled to hve significnt degrdtion t Po.5 nd o.1, respectively. To vlidte the proposed s mesure of mrna integrity, we first exmined the s together with the gene expression profiles of the ssocited smples. Indeed, smples with globl under-representtion of gene expression mtched those with the most negtive s (Fig. 2b). To hve more direct nd quntittive ssessment, we compred gene expression profiles of the 479 smples tht re profiled by both RNA-Seq nd exon microrrys. We rgued tht the extent of degrdtion is reflected in the discrepncy between the RNA-Seq nd microrry dt, since the ltter is not ffected by prtil RNA NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

4 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 Smple 1 Smple 2 b 1. Red coverge 2. Cum. coverge 5 mrna 3 5 mrna RNA-Seq 3 h h Reltive expression 3 d d c Corr. of RNA-Seq nd HuEx (SAC) KS1 KS= hd / h KS Less 3. mks mtrix 3 bis R=.58 More Observed quntile.2 Degrded Not degrded d 4. Fit norml dist. μ=.67 σ= Norml quntile Not degrded Degrded RNA-Seq HuEx RNA-Seq HuEx Reltive expression 3 Cum. Prob Clc. P vlue P=.5 2 std Figure 2 effectively mesures mrna integrity from RNA-Seq dt. () Schemtic illustrtion of the lgorithm to estimte. After estimtion of the 3 bis of ech gene nd smple using KS sttistic from the red coverge profile, n is clculted for ech smple. A norml distribution of the s of non-degrded smples is estimted using mixture model to ssess the sttisticl significnce. (b) Globl under-representtion of gene expression of the BrinSpn smples s mesured by RNA-Seq is ssocited with low s. Smples in the br plot nd the het mp re in the sme order. (c) Vlidtion of s mesure of mrna integrity by direct comprison of the RNA-Seq nd exon rry dt. This nlysis included 479 smples whose gene expression ws quntified by both RNA-Seq nd exon rrys. For ech smple, the correltion of gene expression estimted from RNA-Seq nd tht estimted from exon rrys (denoted seq rry correltion or SAC) is clculted. SAC is plotted ginst the of ech smple (Person correltion R ¼.58, Po , F-test). (d) ws used to seprte 124 smples with the most severe RNA degrdtion (o.33, Po.1, Methods) from the remining 355 smples. For ech group, the het mps of gene expression s mesured by RNA-Seq nd exon rrys re shown, with genes nd smples in the sme order s determined by hierrchicl clustering of the rry dt. frgmenttion. Therefore, for ech smple, we clculted the correltion of the gene expression profiles s mesured by the two pltforms (tht is, seq rry correltion), nd exmined how seq rry correltion reltes to. The two mesures re strongly correlted with ech other (Person correltion R ¼.58, Po , F-test; Fig. 2c). We seprted the smples into two groups depending on (using conservtive threshold:.33 or P ¼.1), nd exmined the gene expression profiles of ech group s mesured by the two pltforms (Fig. 2d). For the group of smples with no or miniml degrdtion, the gene expression profiles mesured by the two different pltforms re indeed very similr. In shrp contrst, the smples inferred to be severely ffected by degrdtion hve drstic difference in their expression profiles s mesured by the two pltforms, with vst under-representtion in the RNA-Seq dt. On the bsis of these observtions, we conclude tht mrna degrdtion due to prtil frgmenttion cn hve mrked impct on globl expression profiles mesured by RNA-Seq, nd cn be used to quntify such degrdtion. Comprison of with nd other QC metrics. We next investigted how reltes to, which is currently stndrd mesure of RNA qulity. Since mtched s re currently not relesed publicly for RNA smples in the BrinSpn dt set, we performed second set of nlyses on lrge-scle humn brin RNA-Seq dt set obtined from the GTEx project 6. This dt set included 41 brin smples with mtched numbers. Alignment of the RNA-Seq dt, QC nd gene expression quntifiction with RNA-SeQC 12 were performed by the GTEx project tem 6 nd were directly used in our nlysis. To minimize technicl vritions introduced in RNA-Seq librry preprtion nd sequencing, we conservtively limited our nlysis to 317 smples with reltively high rte of red mpping (67%). In this subset, 57 smples hve n 8 (18%), 144 smples between 7 nd 8 (45%) nd 116 smples o7 (37%); these smples re from cortex, bsl gngli, cerebellum nd other brin regions from multiple donors (Fig. 3; Supplementry Dt 2). We estimted the mks mtrix nd s of the 317 smples from nucleotide-resolution red coverge profiles (Supplementry Dt 2). When the smples were ordered by s, it is pprent from the mks mtrix tht those with low s in generl hve stronger 3 bis nd thus lower s, but this is not lwys the cse (Fig. 3b). Similr to the BrinSpn dt set, s pproximtely follow norml distribution, with hevier til on the left side indicting degrded RNA smples. By fitting this norml distribution, we estimted tht 1 (3%) nd 82 (26%) smples were degrded t Po.5 nd o.25, respectively 4 NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

5 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 ARTICLE (6,7) (14) <6 (12) 8+ (57) (7,8) (144) b c 1.8 Obs. quntile μ=.34 σ=.33 Spinl cord Substnti nigr Amygdl Hypothlmus Cb Cb (hemisphere) Region Ctx Ctx (BA9) Ctx (BA24) BG (cudte) BG (putmen) BG (nucleus ccumbens) Hippocmpus mks mtrix Less 3 Bis More Cum. prob..6 Norml quntile.4.2 P= std.5.1 d.1 R 2 =.24 e.1 R 2 =.59 f.6 R 2 = RNA-SeQC (5 Norm/3 norm) 5 Norm/3 norm Figure 3 Comprison of with nd other QC metrics. () A summry of 317 GTEx brin smples with respect to numbers nd brin regions. BG, bsl gngli; Cb, cerebellum; Ctx, cortex. (b) The mks mtrix is shown with smples ordered by s nd genes ordered by hierrchicl clustering. Smple s nd s re shown t the top in the sme order. (c) Cumultive distribution of s. A hevier til on the left side reflecting degrded smples is present, while the remining smples fit well with norml distribution s shown in the qqplot in the inset. P vlues re estimted from the estimted norml distribution. (d,e) Correltion between nd (d), between RNA-SeQC metric nd (e) nd between RNA-SeQC metric nd (f). Squred Person correltion is indicted in ech sctter plot. (Fig. 3c, Methods). We note the estimtion of s is extrordinrily robust with respect to the set of genes used for nlysis, nd genes rndomly divided into two subsets gve essentilly the sme results (R 2 ¼.9998, Supplementry Fig. 2). When we compred nd quntittively, 24% of vrince in s cross smples cn be explined by their s (Po , F-test; Fig. 3d). Among the 57 smples with 8, the smllest P vlues re.16 nd.17, while the remining 55 smples (96%) hve P4.25. In contrst, mong the 22 smples with r6, only 6 (27%) hve P4.25. Therefore, the concordnce of nd is remrkble, lthough the two methods estimte RNA integrity using very different pproches with different underlying ssumptions. Severl other tools hve been developed recently to guge 3 bis nd flg-degrded RNA smples 12,13, lthough systemtic evlution of their performnce in lrge-scle dt sets hs not been reported. Among these, RNA-SeQC clcultes the red coverge t the 5 or 3 end of ech trnscript (for exmple, the first or lst 5 nucleotides used in the GTEx dt set) normlized by the coverge throughout the trnscript. These quntities re verged cross ll genes, denoted 5 Norm nd 3 Norm, to evlute the overll 3 bis of the smple. We found good correltion between nd the 5 Norm/3 Norm rtio (R 2 ¼.59, Fig. 3e), nd, to lesser extent, between nd ech of the two metrics (Supplementry Fig. 3,b). This is not surprising becuse both nd RNA-SeQC quntify 3 bis. However, the 5 Norm/3 Norm rtio by RNA-SeQC explins only 15% of vrince in s nd even less if the two metrics were used seprtely (Fig. 3f; Supplementry Fig. 3c,d). Furthermore, no correltion between nd the 5 Norm/3 Norm rtio remins fter is controlled (R 2 ¼.1, P ¼.84, F-test; Supplementry Fig. 3e). The better concordnce of with suggests the dvntge of over RNA-SeQC in quntifying 3 bis with crefully designed sttisticl model. As we rgued erlier, provides very robust mesure of RNA integrity but does not necessrily cpture ll spects of mrna degrdtion. This is in line with the imperfect correltion of nd, lthough the comprtive dvntge of the two metrics hs to be ssessed by n independent mesure, for exmple, by evluting how ech method detects ltertions in gene expression profiles resulting from RNA degrdtion. Since independent expression quntifictions immune to 3 bis re not vilble for the GTEx dt set, we sought n lterntive strtegy. Specificlly, we rgue tht smples with high nd (8 nd, respectively) hve miniml degrdtion nd cn be used s surrogte of the reference trnscriptome. The expression correltion of the remining smples with the reference smples is expected to decrese depending on the severity of degrdtion. To minimize the heterogeneity of gene expression mong different brin regions, we nlysed smples from the cortex, cerebellum nd bsl gngli seprtely, s these regions disply distinct expression profiles (Supplementry Fig. 4). When we ordered smples by or, it becme cler tht subset of corticl nd bsl gngli smples with low or hve distinct gene expression profiles compred with high-qulity smples in these regions, lthough this is less pprent for the cerebellum (Fig. 4 c, top pnels). Quntittively, both nd effectively predicted the correltion of ech smple with NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

6 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 Cortex (76 smples) b Cerebellum (59 smples) c Bsl gngli (81 smples) By By By By By By Cor. with Ref Reltive expression 3 R=.38 R=.48 R=.24 R= R=.5 R= Figure 4 Comprison of nd in predicting ltered gene expression profiles. () Top pnel: gene expression profile of GTEx corticl smples ordered by nd, respectively. Bottom: sctter plot showing (left) nd (right) of ech smple (x xis) nd their medin correltion of expression with subset of high-qulity reference smples (y xis). (b,c) Similr to, but for smples from cerebellum (b) nd bsl gngli (c). Note tht log2 expression vlues re medin-centered to highlight the contribution of genes with vrible levels mong smples to the correltion metrics. the high-qulity reference (Fig. 4 c, bottom pnels, Person correltion R between.24 nd.5 for nd.34 nd.48 for ). Importntly, is more predictive thn in the cortex nd cerebellum. Therefore, is very comprble to in isolting degrded smples with ltered expression profiles, lthough the two methods pper to cpture both shred nd distinct spects of mrna degrdtion, which cn be explined in prt by the source of degrdtion modelled by ech method. Gene-specific degrdtion nd distinct trnscript fetures. Besides per-smple summry of mrna integrity by, our method hs the dvntge of being ble to evlute gene-specific degrdtion. In both the BrinSpn nd GTEx dt sets, not ll genes pper to be ffected by 3 bis to the sme degree, s one cn tell from the expression profiles (Fig. 2b,d; Fig. 4 c), but more directly, from the mks mtrices (Figs 2 nd 5). To investigte the mechnism of gene-specific degrdtion, we clculted gene integrity score (GIS) by correlting mks vlues of ech gene nd the ssocited s cross ll smples (Supplementry Dt 3). We found tht GIS vlues estimted from the GTEx dt set nd the BrinSpn dt set re highly correlted (Spermn r ¼.6, Po , F-test; Fig. 5b). This is remrkble, given tht the mks mtrices of the two dt sets were clculted from red coverge profiles t different resolutions (per-exon for BrinSpn nd per-nucleotide for GTEx). Not surprisingly, the use of insted of to clculte GIS resulted in quntittively similr scores (Supplementry Fig. 5), s one would expect from the correltion between nd. In ddition, most genes hve negtive GIS, suggesting more severe 3 bis in more degrded smples in generl, lthough the degree vries. Our nlysis thus focused on GIS estimted from the GTEx dt set. We sked whether there is ny functionl bis of genes ssocited with trnscript stbility in post-mortem smples. Since the GIS scores show continuum without obvious cutoffs to seprte different groups of genes, we exmined the top nd bottom 1, genes nd their gene ontologies 22. The top 1, genes with the most unstble trnscripts show significnt enrichment in those involved in gene expression regultion, such s chromtin modifiction (Benjmini flse discovery rte (FDR) o.2; Fig. 5c). Genes involved in mrna processing lso pper to be enriched, lthough somewht modertely (Benjmini FDR ¼.18; Supplementry Dt 4). On the other hnd, the bottom 1, genes with the most stble trnscripts re enriched in ribosome subunits nd extrcellulr proteins (Benjmini FDR o5 1 4 nd o5 1 14, respectively; Fig. 5c; Supplementry Dt 5). These observtions suggest tht the degrdtion process is not completely rndom. Encourged by this finding, we lso investigted wht trnscript fetures cn predict their stbility s reflected in GIS. We found longer trnscripts re more unstble nd the trnscript length lone explins bout 15% of the vrince in GIS (Po , F-test); inclusion of 3 untrnslted region (UTR), coding sequence (CDS) nd 5 UTR sizes, s well s exon numbers together with the trnscript length in liner regression model only modertely incresed the explined vrince (16.6%, Po , F-test; Fig. 5d; Supplementry Fig. 6). We lso exmined bse composition (GC content) in different regions of the trnscripts, s well s AU-rich elements (AREs) nd PUM2 motif sites in 3 UTRs, which re reported to ffect mrna stbility Bse composition nd regultory sequences ech explin 3.4 nd 2.4% of the vrince in GIS (Po , F-test). Interestingly, while unstble trnscripts tend to hve higher AU-content in 3 UTR nd CDS, they tend to hve higher GC content in the 5 UTR (Supplementry Fig. 7). Both ARE nd PUM2 motif sites re ssocited with unstble 6 NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

7 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 ARTICLE Low High Gene integrity score (GIS) High Less 3 Bis More Low c Enriched GO term Chromtin modifiction Estblishment of protein locliztion Protein trnsport Nucler lumen Golgi pprtus Methyltrnsferse complex Log 1 (Benjmini FDR) Trnsltionl elongtion Cellulr defence response Extrcellulr region Ribosoml subunit Kertin filment Cytosolic prt Structurl constituent of ribosome b BrinSpn GIS Spermn ρ=.6 d Ctegory Trnscript length GC content Regultory elements Vribles Totl length, exon number, 5 UTR length, CDS length, 3 UTR length 5 UTR GC, coding GC, 3 UTR GC ARE, PUF Vrince explined (%) Combined All vribles bove GTEx GIS Figure 5 Gene-specific degrdtion nd their functionl nd trnscript structurl fetures. () Visuliztion of the mks mtrix with smples ordered by nd genes ordered by gene instbility score (GIS) defined s the correltion between nd mks cross smples. (b) Smooth sctter plot of GIS estimted from the GTEx nd BrinSpn dt sets, respectively. Ares in drker blue represent higher density of genes. (c) Gene ontology (GO) nlysis of genes with the lowest or highest GIS. Only representtive GO terms with Benjmini FDR o.5 re shown (see Supplementry Dt 4 nd 5 for complete list). (d) Liner regression nlysis of GIS using different groups of fetures reflecting trnscript length, bse composition nd regultory sequences. Individul vribles showing positive or negtive correltion with trnscript stbility re indicted in red nd blue, respectively. Vrince explined by ech regression model including specific subset of vribles is shown in the br plot. trnscripts, even fter normliztion of 3 UTR length (Supplementry Fig. 8). When ll these vribles were included in multiple regression nlysis, totl of 2.8% of the vrince in GIS cn be explined (Fig. 5d). Therefore, while these fetures re insufficient to predict the stbility of individul trnscripts, they re predictive for the popultion verge mong groups of trnscripts tht shre these fetures. Discussion For humn trnscriptome studies tht rely on post-mortem tissues, the first nd probbly the most chllenging issue is RNA degrdtion during smple collection due to the occurrence of tissue necrosis, so tht prtilly degrded smples hve to be used in certin scenrios. Similr technicl chllenges re likely present in the collection of smples tht require prolonged processing prior to RNA extrction (for exmple, lser cpture microdissection). In this study, we developed sttisticl method to evlute mrna integrity directly from RNA-Seq dt by quntittively modelling the 3 bis of red coverge. Appliction of this method to two independent, lrge-scle RNA-Seq dt sets profiling humn postmortem tissues demonstrted tht degrded RNAs cn result in strong 3 bis tht mrkedly skews globl gene expression profiles with stndrd pipelines of dt processing. Such bis my msk genuine biologicl differences such s brin region-specific expression or expression vrition mong different individuls, if it is not properly controlled. To our knowledge, this is the first systemtic chrcteriztion of mrna degrdtion in RNA-Seq dt derived from post-mortem smples on such lrge scle. Our nlysis wrrnts dditionl cution in processing dt from smples ffected by degrdtion. Whether such smples should be excluded for nlysis lrgely depends on specific studies nd the extent to which the resulting bis cn be minimized by computtionl normliztion methods. In prticulr, the decision cn differ between the originl studies generting the dt sets nd follow-up studies tht perform re-nlysis or met-nlysis of published dt. Unfortuntely, the qulity of RNA smples used for RNA-Seq could be somewht obscure for follow-up nlysis of published dt, nd smple s re sometimes not reported. Our intention ws thus to develop computtionl method to provide post hoc ssessment of mrna qulity directly from RNA- Seq dt independent of or other prior knowledge in the context of met-nlysis. We demonstrted the effectiveness of our method in comprison with nd other existing QC metrics in isolting degrded smples. It lso hs to be emphsized tht number of pproches nd softwre tools hve recently been developed to normlize bises in RNA-Seq dt introduced by mrna degrdtion nd other technicl issues 12,13, Therefore, cn be potentilly incorported into some of these methods to ddress these issues. In living cells, control of mrna stbility is criticl for both mrna surveillnce nd post-trnscriptionl gene expression regultion. The moleculr mechnisms underlying such controls re complex nd only prtilly understood 15,16. RNA degrdtion pthwys involve number of RNses, including endonucleses tht cut inside the trnscripts nd 5 nd 3 exonucleses tht digest RNA from the two termini. These RNses re ssembled NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

8 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 into different multiprotein complexes coupled with other steps of RNA processing such s decpping nd dedenyltion 15,32,33. Their substrte specificity is conferred by mny co-fctors nd specific regultory sequences embedded in the trget trnscripts. Through comprison of the expression profiles s mesured by RNA-Seq nd exon microrrys, our nlysis suggests tht prtil frgmenttion of mrna is mjor source of bis in RNA-Seq dt derived from post-mortem tissue smples. How such frgments re generted nd ccumulted during tissue necrosis is not cler, but the process might be combined consequence of severl possible mechnisms 4. For exmple, ctive endonucleolytic clevge cn occur in specific trnscripts when the cellulr mrna decy mchineries initilly remin ctive, which cn be followed by lekge of extrcellulr RNses (such s RNse A) into the cells nd stochstic RNA hydrolysis in the deteriorting cellulr environment. An unexpected finding of this study is tht there seems to be reproducible nd gene-specific component of mrna degrdtion in post-mortem smples, s recently noted in nother tissue type 4. Intriguingly, our nlysis reveled tht trnscript stbility is ssocited with distinct functionl groups nd structurl fetures, nd tht these observtions gree well with previous cell-bsed studies of mrna hlf-lives For exmple, genes plying regultory functions, including chromtin-modifying enzymes, hve been found to be unstble t both trnscript nd protein levels, while trnscripts encoding housekeeping genes, such s the ribosome nd extrcellulr proteins, re quite stble. Trnscript length nd regultory sequences previously found to be ssocited with stbility re lso predictive in our nlysis. These observtions imply tht mrna degrdtion in post-mortem smples might be reminiscent of the ctive decy process in living cells depending on intrinsic properties encoded in the trnscripts so tht the high-level orgniztion of the trnscriptome cn be recpitulted in our nlysis. Further support of this ctive degrdtion model comes from severl recent studies demonstrting tht endonucleolytic clevge in the mmmlin trnscriptome, including the brin, is more widespred thn previously pprecited This finding is prticulrly exciting becuse of technicl chllenges in globl mesurements of mrna turnover. Specificlly, current methods rely on pulse lbelling of nucleosides or inhibition of trnscription 35,4,41 to uncouple mrna synthesis nd degrdtion 34,39,4, which hve essentilly limited their ppliction in cell cultures. We re therefore tempted to propose GIS s n lterntive to evlute mrna stbility in ntive tissues, lthough this strtegy hs to be evluted more extensively in future studies. Methods The sttisticl model to quntify mrna integrity. provides singleprmeter mesurement of mrna degrdtion, or more specificlly prtil mrna frgmenttion, for ech smple. This method is bsed on the globl 3 bis of RNA-Seq red coverge in librries prepred from degrded RNAs using the stndrd protocols, including poly-dt selection. In this study, we used RefSeq trnscripts s gene models to quntify 3 bis; for genes with multiple RefSeq trnscripts, the longest trnscript ws used s representtive. To void mbiguity in red ssignment to genes, only those genes without n overlp with other neighbouring genes were included in this study. After mpping RNA-Seq reds to the reference genome nd exon junctions, the mpped reds re counted in ech exonic nucleotide to obtin the red coverge profile long mrna trnscripts. The coverge profile is then trnsformed into cumultive distribution t nucleotide resolution. Denote the number of reds t ech exonic position x (strting from the 3 end of ech trnscript) s n x.the cumultive red coverge is denoted s cx ðþ¼ X x n i¼1 i; ð1þ nd the totl nucleotide coverge is thus h ¼ cl ð Þ; ð2þ where L is the trnscript length. When there is no degrdtion of RNA or technicl bis during librry preprtion nd sequencing, RNA-Seq reds re expected to be uniformly distributed (the null distribution). In prctice, certin mount of non-uniformity is lwys present, nd mjor source of such non-uniformity is the 3 bis due to RNA degrdtion. Such devition from the null distribution cn be quntified by modified KS sttistic 2 : p KS ¼ ffiffiffi pffiffiffi pffiffiffi h supffðþ x=l x g ¼ h supfhf ðþ hx=l x g=h ¼ h d=h; ð3þ where f(x) is the cumultive red coverge distribution t position x, f(x) ¼ c(x)/h. Note tht the direction of devition is distinguished in d, so tht the KS sttistic is positive in the presence of 3 bis nd negtive in the presence of 5 bis. To ccount for dditionl gene-specific bis (such s locl bse composition or errors in trnscript nnottion) tht cnnot be explined by the 3 bis, the KS sttistics re medin-centred cross ll smples for ech gene to obtin mks vlues. The of ech smple is defined s the negtive of verge mks sttistics of ll N genes: ¼ 1 X N N mks g¼1 g: ð4þ The negtion is used so tht degrded smples with 3 bis hve smller s nlogous to. We note tht s of non-degrded smples should follow norml distribution with pproximtely zero men m ¼. This is bsed on the ssumption tht the mks sttistics of different genes in smple without degrdtion re independently nd identiclly distributed. of the smple, which is the negtive verge of the mks sttistics cross lrge number of genes, is expected to converge to norml distribution bsed on the centrl limit theorem. On the other hnd, degrded RNA smples re expected to hve more negtive s, resulting in hevier til on the left side of the distribution. We cn estimte the s.d. of s of the null distribution (no degrdtion), denoted s s, from smples with positive s (which re lest degrded) by the scled medin bsolute devition. For ech smple, z-score nd P vlue cn then be derived to estimte the probbility of observing specific under the null hypothesis (using single-sided test): m z ¼ ; ð5þ s z z 1 P ¼ Nðo j ;1Þdo ¼ p ffiffiffiffiffi e o2 =2 do: ð6þ 1 1 2p where f(o) ¼ N(o,1) is the probbility density function of the stndrd norml distribution. We lso consider scenrio in which reltively lrge number of smples in the dt set might be degrded, such tht the men of non-degrded smples is shifted from zero. To ddress this concern, we sought to develop more robust pproch to estimting the null distribution. In brief, we ssume smples with s bove certin cutoff re non-degrded nd these smples follow truncted norml distribution, whose prmeters cn be estimted nlyticlly. For simplicity, suppose we lredy know the cutoff of the nd smples with 4 re regrded s non-degrded smples. s of the non-degrded smples follow truncted norml distribution, fð j m; s; ; bþ ¼ Nð j m; sþ= ð7þ where ¼ R b Nmj ð m; sþdm nd b ¼þN. The men nd vrince of the truncted norml distribution re 42 m þ f ð Þ fðbþ s; ð8þ nd " s 2 1 þ fðþ bfðbþ f ð Þ fb ð Þ # 2 : ð9þ The prmeters m nd s cn be derived from the moment estimtor by solving the following eqution system, 8 9 < m þ f ðþ fb ð Þ s ¼ M = 2 : s 2 1 þ fðþ bfðbþ ð Þ fb ð Þ ¼ S 2 ; ; ð1þ f where M nd S 2 re the estimted men nd vrince of the s of the (truncted) non-degrded smples, respectively. Since we do not ctully know, we perform step serch of the optiml from.5 to.5, stepped by.5 nd clculte the estimted prmeter m nd s. For ech, the goodness of fit is evluted by compring the cumultive distribution of truncted s nd smples from the fit norml distribution using KS sttistic D (Supplementry Fig. 1). The threshold tht gives the smllest D is chosen. The estimted norml distribution is then used to clculte the z-score nd P vlue of ech smple. BrinSpn RNA-Seq nd Affymetrix HuEx exon rry dt. For this study, we used n RNA-Seq dt set of developing brin trnscriptomes generted s prt of 8 NATURE COMMUNICATIONS 6:7816 DOI: 1.138/ncomms & 215 Mcmilln Publishers Limited. All rights reserved.

9 NATURE COMMUNICATIONS DOI: 1.138/ncomms8816 ARTICLE the BrinSpn project ( ccessed in Mrch 213). This ws the ltest version vilble t the time of nlysis, but n updted version s since become vilble t the time of writing. This dt set is composed of lrge pnel of post-mortem humn brin smples tht spn 13 developmentl stges including fetl, neontl nd dult brins. For ech donor, smples were collected from 8 to 16 brin structures. A totl of 578 smples were profiled by RNA-Seq using poly-dt-selected mrnas. In prllel, the BrinSpn project lso used Affymetrix HuEx ST exon microrrys to profile gene expression on lrgely overlpping set of smples. In totl, the exon rry dt set is composed of 492 smples, mong which 479 hve mtched RNA-Seq dt. Poly-dT selection ws not used in preprtion of cdnas for exon rry hybridiztion; insted, cdnas were generted by rndom priming, becuse rrnas re not expected to hybridize with probes designed for mrnas. The RNA-Seq dt were vilble publicly s red per kilobse per million (RPKM) vlues t the exon nd gene level (GENCODE V3C). These vlues were derived from red mpping using ELAND2, followed by quntifiction using RSEQtools 43. The exon rry dt were vilble s exon nd gene intensities normlized by the RMA lgorithm implemented in the Affymetrix Power Tools. More detils of dt processing s performed by the BrinSpn project tem were described in their documenttion vilble t These processed files were downloded from downlod.html. To perform nlysis, we used red coverge profiles derived from exon RPKM vlues, becuse the nucleotide-resolution red coverge profiles or the rw dt were not vilble. In this cse, the totl red coverge h (verge RPKM L) reflects the bundnce nd length of ech trnscript, but not the sequencing depth of ech smple. However, ssuming the sequencing depth is reltively uniform cross different smples, we do not expect this to hve significnt impct on the results. To reduce uncertinty, we estimted the KS sttistic only for genes with RPKM 42 in ech smple nd ssigned missing vlue otherwise. After we obtined the mks mtrix, 7,783 genes with mks estimted in 45% of smples were used to clculte s. For this study, we used the mixture model to estimte the prmeters of the null distribution ( ¼.3, m ¼.66 nd s ¼.31). Centroid linkge hierrchicl clustering of gene expression profiles presented in Fig. 1 used 9,75 of 18,979 genes in the RNA-Seq dt (RPKM43in1 smples, log 2 -trnsformtion, s.d.41 nd then medin centred) nd 6,55 of 17,64 genes in the microrry dt (log 2 intensity 47 in1 smples, s.d.4.6 nd medin centred). To determine the order of smples nd genes described in Fig. 2d, we focused on 6,55 genes tht pssed filtering in exon rry dt. Among these, 6,319 were common in the RNA-Seq dt nd were used for direct comprison. A hierrchicl clustering ws performed on these common genes cross the 479 common smples using the exon rry dt. The smples were then divided into two groups with threshold of ¼.33 (P ¼.1). In ech group, het mp ws generted with the predetermined order using ech dt set. GTEx RNA-Seq dt. The current relese of the GTEx dt set (phe6.v1, relese dte: 17 Jnury 214) included totl of 41 brin smples with RNA-Seq dt, mtched s, gene expression quntifiction nd RNA-SeQC results, which were obtined from the GTEx project through dbgp ( nih.gov). Among these, 317 smples with red mpping rte.67 (bsed on RNA-SeQC results) were used in our nlysis, becuse our initil exmintion of the gene expression profiles suggested tht smples with low mpping rte hve lower correltions with the other smples. There pper to be few dditionl outlier smples s judged from their expression profiles (Fig. 4), but we did not find pprent bnormlities in QC metrics, so they were not excluded. Alignment of RNA-Seq reds (by TopHt 44 s provided by the GTEx project tem) were extrcted from bm files using bedtools 45, which were converted into red coverge profiles long exonic positions of representtive RefSeq trnscripts. We estimted KS sttistics for ech gene nd smple with verge red coverge h/l2 nd RPKM2 (nd missing vlue otherwise). To reduce potentil noise in estimting, we pplied filters of genes bsed on KS sttistic nd gene expression RPKM vlues clculted by RNA-SeQC. We first excluded the top 5% of genes with the smllest s.d. in their KS sttistics cross smples. To remove genes tht show strong 5 bis, typiclly due to errors in trnscript nnottion, lterntive RNA processing or other technicl issues, we excluded genes with medin KS sttistic cross smples o. Finlly, 9,168 genes with KS estimted in 45% smples were kept to derive the mks mtrix nd clculte for ech smple. The prmeters of the null norml distribution were estimted to be ¼.35, m ¼.34 nd s ¼.33. To evlute gene-specific integrity, we focused on 8,493 of 9,168 filtered genes bove tht re protein-coding genes with nnotted 5 UTR, CDS (whose length is multiple of three) nd 3 UTR. GIS is defined s the Person correltion between mks vlues cross ll smples nd their ssocited s. To correlte GIS estimted from the GTEx nd BrinSpn dt sets, we considered only genes with 5 exons, becuse for the BrinSpn dt set the red coverge profile is vilble only t the per-exon level, hence it is more difficult to obtin precise estimte of KS vlues nd thus GIS for genes with single or few exons. To clculte the correltion of gene expression profiles cross smples, s presented in Fig. 4, we required genes to hve RPKM 43 in1 smples to eliminte low-bundnce genes. RPKM vlues of 11,958 filtered genes re log 2 -trnsformed nd medin centred to clculte correltions of ech smple with subset of high-qulity reference smples (defined by 8 nd ). For ech smple, the medin correltion with the reference ws then clculted. For smple belonging to the reference, the medin ws clculted using the remining smples in the reference set. To generte the gene expression het mps shown in Fig. 4, we further required s.d. cross ll smples 4.6 nd %presence.8 to highlight genes with the most vritions mong smples. A totl of 7, filtered genes were included for centroid linkge clustering nlysis. Trnscript structurl nd functionl fetures. Representtive RefSeq trnscripts used to estimte were used to define the number of exons, whole-trnscript length s well s the lengths of 5 UTR, CDS nd 3 UTR. Exonic sequences of ech of these regions were extrcted to clculte GC content nd serch for motif sites. For AREs, we initilly serched different vritions (for exmple, AUUUA, WWAUUUAWW nd others) 46, but found AUUUA s the only one with significnt correltion with GIS. We therefore focus on this core motif in our nlysis. For the PUM2 motif, we used the consensus UGUAHAUA, s determined by previous studies 26. To relte trnscript stbility with trnscript fetures, we first generted bins of 1 genes nd clculted the verges of ech feture in bins nd their correltion with the verge GIS in respective bins. For regression nlysis, we first pplied Fisher trnsformtion on GIS, ln[(1 þ GIS)/(1 GIS)]/2, so tht it follows pproximtely norml distribution. For the sme reson, we lso pplied log trnsformtion on length of whole trnscripts, s well s lengths of 5 UTR, CDS nd 3 UTR. Liner regression nlyses were first performed using individul groups of vribles nd then in combintion, s summrized in Fig. 5d. Gene ontology nlysis ws performed using the online tool DAVID 22. All genes included for GIS nlysis were used s bckground to compre with the 1, most unstble nd the 1, most stble trnscripts, respectively. Softwre implementtion. A set of perl nd R scripts were implemented to clculte the exonic red coverge, cumultive distribution nd KS sttistics, s nd the sttisticl significnce. 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