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Revealing Missing Human Protein Isoforms Based on Ab Initio Prediction, RNA-seq and Proteomics.

Hu Z, Scott HS, Qin G, Zheng G, Chu X, Xie L, Adelson DL, Oftedal BE, Venugopal P, Babic M, Hahn CN, Zhang B, Wang X, Li N, Wei C - Sci Rep (2015)

Bottom Line: Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples.We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought.In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

View Article: PubMed Central - PubMed

Affiliation: 1] School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China [2] Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Pudong District, Shanghai 201203, China.

ABSTRACT
Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

No MeSH data available.


Related in: MedlinePlus

Validation summary of KNOWN and novel transcripts.A. shows the number of KNOWN and novel transcripts validated by each RNA-seq dataset using standard or stringent strategy. The highest two points in each group represent validated numbers from GROUP II datasets (RNA-seq data sequenced from the 16 tissues mixture). B. shows validated KNOWN and novel transcript numbers using the standard or stringent strategy grouped by numbers of validated datasets. C and D show the extra numbers of validated KNOWN and novel transcripts using standard or stringent strategy when a new RNA-seq dataset was added. This process was simulated 1,000 times using a bootstrapping strategy.
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f3: Validation summary of KNOWN and novel transcripts.A. shows the number of KNOWN and novel transcripts validated by each RNA-seq dataset using standard or stringent strategy. The highest two points in each group represent validated numbers from GROUP II datasets (RNA-seq data sequenced from the 16 tissues mixture). B. shows validated KNOWN and novel transcript numbers using the standard or stringent strategy grouped by numbers of validated datasets. C and D show the extra numbers of validated KNOWN and novel transcripts using standard or stringent strategy when a new RNA-seq dataset was added. This process was simulated 1,000 times using a bootstrapping strategy.

Mentions: A pipeline was created to validate the known and predicted transcripts with RNA-seq data (see lower part of Fig. 1). Coding sequences from MIXTURE transcripts were extracted with 100nts upstream of start codons and 100nts downstream of stop codons. These sequence fragments would be validated using high quality RNA-seq reads. We used 26 public datasets (50 RNA-seq runs), which could be grouped into 3 subgroups based on data sources and read lengths (GROUP I, II and III, Table S1), to validate MIXTURE transcripts. We first checked the validation landscape of KNOWN transcripts. Using the standard strategy (see Methods), we could validate about 10 k ~ 20 k multi-exon KNOWN transcripts from each RNA-seq dataset (Fig. 3A and Table S2); and in total, 40,797 multi-exon KNOWN transcripts (73.94% of all KNOWN transcripts, or 76.91% of KNOWN multi-exon transcripts) were validated, of which, 36,128 transcripts were validated from at least 2 different datasets (Fig. 3B and Table S3). Using the stringent strategy, the number of validated transcripts from each dataset were slightly smaller (Fig. 3A and Table S2); in total, 35,037 multi-exon KNOWN transcripts (63.50% of all KNOWN transcripts, or 66.05% of multi-exon KNOWN transcripts) were validated, of which, 29,068 transcripts were validated from at least 2 datasets (Fig. 3B and Table S3). 5,429 (15.50% of 35,037) transcripts were validated from a specific tissue alone, which implied their tissue-specific expression. Furthermore, 1,992 single-exon transcripts (63.70% of single-exon KNOWN transcripts) were also validated.


Revealing Missing Human Protein Isoforms Based on Ab Initio Prediction, RNA-seq and Proteomics.

Hu Z, Scott HS, Qin G, Zheng G, Chu X, Xie L, Adelson DL, Oftedal BE, Venugopal P, Babic M, Hahn CN, Zhang B, Wang X, Li N, Wei C - Sci Rep (2015)

Validation summary of KNOWN and novel transcripts.A. shows the number of KNOWN and novel transcripts validated by each RNA-seq dataset using standard or stringent strategy. The highest two points in each group represent validated numbers from GROUP II datasets (RNA-seq data sequenced from the 16 tissues mixture). B. shows validated KNOWN and novel transcript numbers using the standard or stringent strategy grouped by numbers of validated datasets. C and D show the extra numbers of validated KNOWN and novel transcripts using standard or stringent strategy when a new RNA-seq dataset was added. This process was simulated 1,000 times using a bootstrapping strategy.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496727&req=5

f3: Validation summary of KNOWN and novel transcripts.A. shows the number of KNOWN and novel transcripts validated by each RNA-seq dataset using standard or stringent strategy. The highest two points in each group represent validated numbers from GROUP II datasets (RNA-seq data sequenced from the 16 tissues mixture). B. shows validated KNOWN and novel transcript numbers using the standard or stringent strategy grouped by numbers of validated datasets. C and D show the extra numbers of validated KNOWN and novel transcripts using standard or stringent strategy when a new RNA-seq dataset was added. This process was simulated 1,000 times using a bootstrapping strategy.
Mentions: A pipeline was created to validate the known and predicted transcripts with RNA-seq data (see lower part of Fig. 1). Coding sequences from MIXTURE transcripts were extracted with 100nts upstream of start codons and 100nts downstream of stop codons. These sequence fragments would be validated using high quality RNA-seq reads. We used 26 public datasets (50 RNA-seq runs), which could be grouped into 3 subgroups based on data sources and read lengths (GROUP I, II and III, Table S1), to validate MIXTURE transcripts. We first checked the validation landscape of KNOWN transcripts. Using the standard strategy (see Methods), we could validate about 10 k ~ 20 k multi-exon KNOWN transcripts from each RNA-seq dataset (Fig. 3A and Table S2); and in total, 40,797 multi-exon KNOWN transcripts (73.94% of all KNOWN transcripts, or 76.91% of KNOWN multi-exon transcripts) were validated, of which, 36,128 transcripts were validated from at least 2 different datasets (Fig. 3B and Table S3). Using the stringent strategy, the number of validated transcripts from each dataset were slightly smaller (Fig. 3A and Table S2); in total, 35,037 multi-exon KNOWN transcripts (63.50% of all KNOWN transcripts, or 66.05% of multi-exon KNOWN transcripts) were validated, of which, 29,068 transcripts were validated from at least 2 datasets (Fig. 3B and Table S3). 5,429 (15.50% of 35,037) transcripts were validated from a specific tissue alone, which implied their tissue-specific expression. Furthermore, 1,992 single-exon transcripts (63.70% of single-exon KNOWN transcripts) were also validated.

Bottom Line: Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples.We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought.In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

View Article: PubMed Central - PubMed

Affiliation: 1] School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China [2] Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Pudong District, Shanghai 201203, China.

ABSTRACT
Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

No MeSH data available.


Related in: MedlinePlus