Table 1. List of Published Orthogonal Data Assisted Proteomics Studies.
| Genomic Information | ||
|---|---|---|
| Choudhary et al. | six-frame translation using the draft of human genome | (13) |
| Fermin et al. | six-frame translation of whole human genome | (19) |
| Sevinsky et al. | six-frame translation of whole human genome | (18) |
| peptide isoelectric point (pI) | ||
| Bitton et al. | prescreening searches on databases translated from individual chromosomes; matched entries were then combined with the Celera database entries and used for a second time search | (12) |
| Mo et al. | exon–exon junction database | (29) |
| Power et al. | noncontiguous junction peptides in a “full length transcript” | (30) |
| Gatlin et al. | generating dynamically all possible SNPs | (40) |
| Roth et al. | creating a highly annotated database, including splicing, PTMs, and SNPs | (47) |
| Bunger et al. | reference protein database | (41) |
| tryptic peptide database created from dbSNP | ||
| peptide pI | ||
| Schandorff et al. | elongating IPI sequences with theoretical N-terminal peptides, variant peptides from cSNP, variant peptides from conflict annotation in Swiss-Prot, and proteolytic enzyme and keratin sequences | (37) |
| Xi et al. | human disease-related variants from OMIM, PMD, and Swiss-Prot | (44) |
| Nijveen et al. | 20-mer variant peptides generated by three-frame translation from mRNA sequences including SNPs in dbSNP | (35) |
| Li et al. | combined database of normal proteins and variant peptides | (46) |
| modified FDR estimation | ||
| Su et al. | a pipeline of nontargeted proteomics for identifying SAP peptides in human plasma and quantifying them using targeted proteomics | (43) |
| Khatun et al. | whole genome proteogenomic mapping to identify novel protein coding regions for ENCODE cell line proteomics data | (8) |
| Transcriptomic Information | ||
|---|---|---|
| Tanner et al. | using genomic data and EST data to construct the exon graph, which is a compact representation of all putative exons, splice variants and polymorphisms | (31) |
| Edwards et al. | using sequence database compression strategies to reduce EST database size by approximately 35-fold | (36) |
| Menon et al. | three-frame translation of mRNA sequences from the ECgene and ENSEMBL databases | (33),34 |
| Ramakrishnan et al. | using expression information from microarray to assist protein identification | (52) |
| Ning et al. | six-frame translation of novel junction mRNA sequence identified by RNA-Seq | (38) |
| Wang et al. | customized database from RNA-Seq data | (49),59 |
| Chen et al. | generating database for missense SNVs and RNA edits from genomic sequencing and RNA-Seq data | (48) |
| Sheynkman et al. | deriving novel splice-junction peptides from RNA-Seq data | (39) |
| Evan et al. | de novo assembly of transcriptomes from RNA-Seq data | (51) |
| Menschaert et al. | A custom protein database built from both Swiss-Prot and RIBO-seq derived translation products | (61) |
| Woo et al. | A proteogenomic database from large scale RNA-Seq data | (26) |
| Sheynkman et al. | detection of variant peptides from RNA-Seq data | (62) |
| Network Information | ||
|---|---|---|
| Li et al. | protein–protein interaction network-assisted protein assembly through clique enumeration and enrichment analysis | (54) |
| Ramakrishnan et al. | improving protein identification by considering information on functional associations from a gene function network | (56) |
| Goh et al. | using functional clusters to expand protein lists | (63) |
| Nusinow et al. | using a network-based inference tool, SNIPE, to select proteins that are likely to be active but undetectable in shotgun proteomics | (55) |