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,) VWHS6L]H>L@ ! PD[BVWHS6L]H 6(7 PD[BVWHS6L]H WR VWHS6L]H>L@ (1' ,) (1' )25 ,) Q,WHU! 0$;B1,

Number of false negatives


Number of false negatives


Estimated transcripts

E R E R E R E R E R E R E R E R E R R
A T C G A C G C G T A T @ A T T T T A G C A C @ T T A G C A C $ G T G C T A A @ G T G C T A A A A T @ A T A C G C G T C G A T $ A T A C G C G T C G A T A T C G A C G C G T A T (a) (b) 

Figure S1 .

 S1 Figure S1. Distribution of read positions and fragment length in the simulated data. (a) Distance from 5'end, (b) distance from 3'end, (c) relative position within a transcript, (d)fragment length, from one of the simulated data sets with 5 million reads each. The plots were obtained directly from the information from the simulated reads not from the mapping, and thus do not include ambiguity introduced by alignment. 


Figure S2 .Figure S3 .Figure S4 .Figure S5 .

 S2S3S4S5 Figure S2. Comparison of accuracy across multiple methods for paired-end RNA-seq. (a) A schematic diagram showing the number of isoforms estimated by individual methods and the commonly estimated ones. Diagrams are not drawn in exact scale. (b)-(e) Four different evaluation criteria are applied to multiple quantifiers on the common set of isoforms indicated in (a). (b) Pearson correlation coefficient (c) root mean squared error (d) number of false positives, i.e., isoforms with zero true expression and with 1 or larger value in log(estimated_TPM• +1), (e) number of false negatives, i.e. isoforms with 1 or larger value in log(true_TPM+1) and zero estimated expression. (f) total number of transcripts whose expression level is reported. EMSAR, RSEM, Sailfish and IsoEM report all transcript levels. Error bars indicate standard deviation, not standard error of mean. 


Figure S6 .

 S6 Figure S6. Comparison of accuracy as measured by concordance to qRT-PCR on real RNA-seq data. The same figure 6, except the inclusion of Sailfish. Pearson correlation between RNA-seq-based gene expression level estimates (log(TPM* +1)) and qRT-PCR-based measurements ( Ct), across independently performed experiments. was used to maximize the correlation to adjust for any scale effect of the pseudocount, which allowed inclusion of zero estimates. Six RNA-seq data sets (UHRR-1 to -4, HBRR-1, MKN-28) and four qRT-PCR sets (UHRR MAQC [19], HBRR MAQC [19], UHRR Wang et al. [20], MKN-28 from Lee et al. [6]) were compared. RNA-seq-based quantification was run on two independent transcriptome models (ENSEMBL (E) and RefSeq (R)). The results from Lee et al. on an older version of RefSeq model (R') was shown to the right for comparison (grey box). Gene-level estimates were obtained by summing the relevant isoform-level estimates, except for NEUMA, for which gene-level estimates were obtained either from the reads common to all the isoforms of a gene or by summing the isoform-level estimates derived from reads unique to individual isoforms. 


Figure S7 .

 S7 Figure S7. Detailed algorithm description of EMSAR. (a)The algorithm of computing segment lengths for unstranded single-end RNA-seq is illustrated using a simplified transcriptome. First, all the transcript sequences (in this case, three) are concatenated followed by their reverse complementary sequences. Then, an array is created to store the start positions of substrings of a specific length (in this case, 6 bp) on the concatenated sequence. The length of the substrings is identical to the read length. Any substring that contains a delimiter character (e.g. '@' or '$') is skipped. Only the alphabetically smaller between a sense substring and its revere complement is stored (eg. position 54 is stored but not 3.). This is to ensure that a substring and its reverse complement are treated as indistinguishable, like the reads from an unstranded RNA-seq experiment. Then, this array is sorted by the associated substrings, to identify clusters of identical substrings and count the shared substrings for each unique combination of transcripts. (b) A simplified algorithm of computing segment lengths for paired-end RNA-seq. First an array containing start positions of all substrings of a specified length is created and sorted, which serves as a mate1 array. Then, for every cluster of identical mate1 substrings, a mate2 array of all start positions that can be paired with mate1 is created within a specified fragment length range, and is sorted by the mate2 substrings. For unstranded data, the mate2 array only includes cases in which the second mate is alphabetically smaller than its reverse complement. The mate2 array is removed after being scanned for counting shared substrings. The length of segment is computed for individual fragment lengths during indexing, and later weighted by the data-specific fragment size distribution. 


Figure S8 .

 S8 Figure S8. Algorithmic details of EMSAR. (a)The data structure for transcriptome index. The data structure is constructed as the suffix array is scanned, can be stored as an index file and reconstructed from the index file, and is used to count reads for each segment as the program reads through RNA-seq alignments. The structure uses relatively stable amount of memory and allows relatively fast access to individual segments. For segments associated with multiple transcripts, the number of transcripts (tsize) and the first transcript ID (tid0) are directly accessed as indices of a two-dimensional array that stores the pointer to a linked list of all segments with the corresponding tsize and tid0. An individual node in the linked list corresponds to a segment and it contains the rest of the transcript ID's (tid's) associated with the segment, as a numerically sorted array. Each node also stores a read count and an array of segment lengths computed for individual fragment sizes. The linked list is sorted by the tid array, as it is created. For segments that consist of a single transcript, a one-dimensional array is used with indices representing transcript IDs. (b) A recursive propagation strategy used for identifying sets of segments. cid: segment ID; tid: transcript ID; sid: sequence-sharing set ID. The first segment (cid=0) is fed to the recursion, in which cid is assigned sid=T and all of its associated tids are also assigned sid=T. Then, for each of the tid's, all of its associated cid's is fed to the same function with sid=T. This process is repeated until no new assignment is possible. Then, pick the next unassigned cid and do the same with sid=T+1. Do this until no more unassigned segment is available. 


Figure S9 .

 S9 Figure S9. Reads with ambiguous fragment size. An example illustration of two isoforms, one with and one without a skipped exon. Top: Two different types of shared reads are shown. The one with unambiguous fragment size (green) is used by EMSAR, whereas the one with ambiguous fragment size (red) is not. Bottom: Various types of transcript-specific reads that are always used by EMSAR (blue). 




  




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