Int J Pharm Pharm Sci, Vol 8, Issue 2, 298-301Original Article


A DE NOVO ASSEMBLY METHOD FOR SHORT SEQUENCE OF SOLID-SAGE READS RESPONSIBLE FOR WHEAT (TRITICUM AESTIVUM L.) LEAF RUST

JYOTI PATHAK, KUNAL MUKHOPADHYAY, RAJU PODDAR*

Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India
Email: rpoddar@bitmesra.ac.in

 Received: 30 Oct 2015 Revised and Accepted: 30 Dec 2015


ABSTRACT

Objective: Wheatleaf rust is one of the most widespread rust diseases caused by Puccinia triticina Eriks. De novo assembly of short sequence reads in order to understand the molecular phenomenon underlying wheat leaf rust interaction and to assemble differentially expressed genes, resistance genes and the genes encoding transcription factors in response to Puccinia infection in wheat was the main objective of the present study.

Methods: De novo assembly of SOLiD (sequencing by oligonucleotide ligation and detection) SAGE (serial analysis of gene expression) sequence reads from a pair of Near-isogenic lines (NILs) of wheat cultivar HD2329 with Lr28 (resistant) and HD2329 lacking Lr28 (susceptible) that were either infected with the most virulent pathogen Puccinia triticina or inoculated as mock in the absence of any reference sequence was carried out using multiple k-mer approach. Combinations of different software working on different algorithm were used to obtain a maximum number of differentially expressed transcripts.

Results: De novo assembly at different k-mers produced a large number of contigs.The size of contigs was further increased with the use of different assembly software. Redundancy was removed both at nucleotide and protein levels, which increased the quality of assembly.

Conclusion: For the assembly of short sequences of the complex genome such as those of polyploids a combination of software gives longer and unique contigs. It may be used in understanding the molecular mechanism of plant-microbe interaction.

Keywords: Wheat,Leaf rust, SOLiD, SAGE, De novo assembly, NILs.


INTRODUCTION

Wheat is one of the major cereals for human nutrition. Wheat consumption worldwide is estimated to surpass 817 million tons by 2030 [1]. Rust diseases are the most widespread and economically important diseases of cereal crops worldwide. Three distinct diseases, leaf rust (Brown rust), stripe rust (Yellow rust) and stem rust (Black rust), and found in wheat. The fungal pathogens that cause these diseases are constantly evolving new virulent form of rust pathotypes and overcome the resistance of wheat varieties. Leaf rust caused by the fungus Puccinia triticina Erikss. & Henn. is the most common and widely distributed of the three wheat rusts. For the control of a mutagenic variety of leaf rust pathogens, more knowledge will be required on the wheat sequences and their function such as in plant-pathogen interaction, signaling mechanism and role of transcription factors in obtaining a better combination of Lr genes and enhanced defense activity during infection.

Wheat genome size is about 17 GB. Whole genome sequence data are still not available. Transcriptome sequencing is considered to be an effective alternative for rapid identification of wheat genes. In the absence of complete reference sequence, De novo assembly of sequencing reads is a key step for comparative study.

De novo sequence assembly is the method of generating contiguous sequences by merging together individual sequence reads. These contiguous sequences or contigs share the same nucleotide sequence as the template (DNA or RNA) from which they are derived. As next generation sequencing technologies are revolutionizing, there is a limitation to the assembly programs used for short sequence data of 20-30 nucleotides such as SOLiD reads. Several algorithms are reviewed for De novo assembly [2-5] and have been implemented by many software packages including CAP3 [6], Velvet [7], Oases [8], Trinity [9], Trans-ABySS [10], but none of them can be individually used for the assembly of solid reads. The main reasons are a large number of sequences generated which makes the analysis computationally costly and base calling errors are more likely to occur due to their short length. Wheat is an allohexaploid containing homeologous and duplicated genes. Wheat contains 80% of repetitive sequences. It becomes difficult to resolve repetitive sequences since; the reads generated by sequencing are shorter than the repetitive unit. This can lead to misassembled or partial assembled contigs. Another limitation to the assembly of short sequence reads is that most of the assemblies have been reported with long sequences [11] or with a combination of short and long reads [12]. Short read technology such as IIlumina has been successful in a wide variety of transcriptome analysis in plants with paired-end sequences [13, 14]. Assembly of single-ended solid sequences is difficult. However, studies [15, 16] support assembly of short sequence reads of 20-30 nucleotides long with gaps. Combinations of De novo assembler have been used to reconstruct high-quality transcriptome in polyploids [17]. We present a method for assembly of short reads where multiple k-mer and combination of assembly software’s based on different algorithms is used. The basic assumption of this is that different k-mers will allow the assembly of transcripts with different abundances. At higher k-mer length more number of contig sequences are assembled which are highly expressed transcripts. On the contrary, at lower k-mer length poorly expressed transcripts are assembled better [7, 18].

MATERIALS AND METHODS

SAGE libraries were prepared for four selected wheat lines; susceptible HD2329 mock (S-M), susceptible HD2329 infected (S-PI), resistant HD2329+Lr28 mock (R-M) and resistant HD2329+Lr28 infected (R-PI) using a Solid SAGE kit (Applied Biosystems, CA, USA). These libraries are named SAGE1, SAGE2, SAGE3 and SAGE4 respectively.

Pre-processing of reads

The reads from the four libraries were imported separately to CLC Genomics Workbench v. 6.5.1. Trimming was performed under the following trim settings: (i) removal of the low-quality sequence (limit = 0.05), trimming of sequences on the basis of quality score on a phred scale. The quality score is converted into error probability. This has high value for low-quality bases. (ii) Removal of ambiguous nt: maximum of 2 nt allowed, (iii) removal of adapter sequences, (iv) removal of sequences on length: minimum length of 20 nt. Since two libraries S-PI and R-PI might contain reads originating from Puccinia transcripts,all post-trimmed reads were mapped to Puccinia transcripts available at The Broad Institute (https://www. broadinstitute.org/annotation/genome/puccinia_group/MultiHome.html). The reads that did not match to Puccinia transcripts were used for de novo assembly. Individual assembly of the four libraries was carried out on a server with 64 GB RAM. Fig. 1 represents the overall strategy for de novo assembly.

Fig. 1: Overview of methodology used for De novo assembly


De novo assembly

De novo assembly was carried out using different software’s based on two different algorithms. CLC Genomics Workbench works on de Bruijn graph (DBG) and CAP3 working on an overlap consensus method (OLC). The OLC algorithm is computationally more intensive than DBG, and most effective for low coverage long read, whereas DBG is more suitable for high coverage short reads especially for large genome assembly. The use of k-mers which are shorter than the read lengths in de Bruijn graphs reduces the computational intensity of this method [19].

Reads from four libraries were taken, and de novo assembly was carried out at different odd k-mers from 13-25 in CLC Genomics Workbench v.6.5.1. CLC bio’s de novo assembly algorithm works by using de Bruijn graph [7]. Reads were mapped back to the contigs with a minimum length fraction of 80% and minimum identity of 90% in order to update the contigs. The advantage of mapping reads to the contigs and selecting ‘Update Contig’ in CLC is that it removes the region in the contig where no read is mapped and for any mismatch to the contig, the sequence is updated to reflect the majority base at that location among the reads mapped there. Assembled sequences obtained from all k-mer were combined to get a maximum number of sequence assemblies. Redundant sequences were removed by using CD-HIT-EST followed by CD-HIT [20] at 95% and 90% identity respectively. Sequences greater than 100 base pairs were selected and taken for further process. Wheat genome is composed of 80% of repetitive sequences. Candidate repetitive elements and transposons were identified based on results from BLASTN and BLASTX searches against the nucleotide and protein Triticea Repeat Sequence Databases (TREP) using an E-value cutoff of 1e-10.

To produce longer, more complete contigs CAP3 was used for further assembly. Different overlap and identity cutoff were tried for assembly and then compared for similarity by BLASTN to wheat Unigene at an E-value cutoff 10-5. Based on the results we obtained the best assembly at the minimum overlap of 50 base pairs and minimum identity of 99% with the following options “-p 99–l 50 v 100”. Removal of redundant sequences at protein level was performed using Evidential Gene tr2aacds pipeline [21] (http://arthropods. eugenes.org/Evidential Gene/about/ Evidential Gene_assembly_pipe.html). It selects the ‘best’ set of De novo assembled transcripts, based on coding potential. The algorithm first produces CDS and amino acid sequences for each sequence and then removes redundant sequences using the amino acid information for choosing the best coding sequences amongst identical sequences. Self-on-self BLAST is then implemented to identify highly similar sequences. The alignment data and CDS/protein identities are then used to select and output transcripts classified as ‘main’ or ‘alternate’, and another set classified as ‘dropped’ which did not pass the internal filters. The primary and alternate De novo assembled transcripts were used for further assessments.

The assembly produced can be used as a reference for applications such as single nucleotide polymorphism (SNP) detection, expression study, gene identification and pathway study.

RESULTS

Table 1, 2, 3 and 4 provides the summary of the results obtained after de novo assembly at different k-mers all odd numbers starting from 13-25. In all four libraries, the largest number of contigs obtained at k-mer 13. However, the average length of contigs and N50 was greatest at k-mer 15. A contig N50 is calculated by first ordering every contig by a length from longest to shortest. Next, starting from the longest contig, the lengths of each contig are summed, until this running sum equals one-half of the total length of all contigs in the assembly. The contig N50 of the assembly is the length of the shortest contig in this list [22].

Table 1: Summary of De novo assembly in CLC genomics workbench at different k-mer for SAGE1 library

K-mer

Total contig

Avg. contig size

%matched reads

No. of transcript>1000bp

Min contig length

Max contig length

N50

13

1 70 576

879

45.57

59 358

16

8026

1299

15

27 336

1266

32.97

14 652

16

9394

1813

17

4331

594

16.55

824

17

16 203

1126

19

3182

463

14.48

384

18

12 858

915

21

5608

424

18.36

592

25

12 858

869

23

6310

389

25.58

615

26

14 238

844

25

3850

373

19.72

329

30

13 080

786

Total

2 21 193

           

Table 2: Summary of De novo assembly in CLC genomics workbench at different k-mer for SAGE2 library

K-mer

Total contig

Avg. contig size

%matched reads

No. of transcript>1000bp

Min contig length

Max contig length

N50

13

1 67 587

886

47.66

58 870

16

9001

1307

15

23 483

1290

32.89

12 686

16

11 239

1853

17

4892

555

17.36

813

17

11 915

1167

19

3171

395

11.64

292

23

12 665

876

21

5324

367

17.27

429

25

13 783

817

23

5463

334

18.27

393

28

13 695

763

25

3319

317

20.47

210

28

8967

708

Total

2 13 239

           

Table 3: Summary of De novo assembly in CLC genomics workbench at different k-mer for SAGE3 library

K-mer

Total contig

Avg. contig size

%matched reads

No. of transcript>1000bp

Min contig length

Max contig length

N50

13

1 61 161

888

48.87

56 577

16

8246

1302

15

12 362

1307

24.44

6729

16

10 817

1917

17

3072

500

11.97

454

20

11 348

1003

19

2560

401

9.49

240

25

6867

805

21

4209

351

14.17

315

25

9275

740

23

3923

330

16.16

252

23

14 238

686

25

2579

322

12.12

155

32

9338

658

Total

1 89 866

           

Table 4: Summary of De novo assembly in CLC Genomics Workbench at different k-mer for SAGE4 library

K-mer

Total contig

Avg. contig size

%matched reads

No. of transcript>1000bp

Min contig length

Max contig length

N50

13

1 51 627

915

51.00

55 661

16

8504

1331

15

11 057

1251

24.04

5706

16

12 768

1873

17

2418

438

9.31

312

17

6824

1120

19

2160

325

9.35

172

24

6824

804

21

3311

295

13.16

202

25

8926

763

23

2705

265

13.99

140

32

6824

624

25

1481

237

10.04

54

35

4220

452

Total

1 74 758

           

The total number of contigs reduced to about 57% after redundancy removal by CD-HIT (table 5). 1 06 816, 1 04 054, 92 625 and 88 309 numbers of contigs greater than 100 base pairs were selected in the four libraries respectively. 661, 636, 480 and 500 repetitive sequences were found after BLASTN and BLASTX to TREP. The sequences obtained after removal of repetitive sequences were assembled using CAP3 which formed 10, 17, 18 and 12 supercontigs in SAGE1, 2, 3 and 4 libraries respectively. Finally, after redundancy removal by Evidential Gene at the protein level, we obtained 45 263, 44 467, 41 556 and 40 023 final contigs in SAGE1, SAGE2, SAGE3 and SAGE4, respectively.

Table 5: Number of contigs after assembly and redundancy removal

Contigs

SAGE1

SAGE2

SAGE3

SAGE4

After CLC

2 21 193

2 13 239

1 89 866

1 74 758

After CD-HIT

1 25 520

1 22 023

1 07 943

1 01 470

>100 base pair

1 06 816

1 04 054

92 625

88 309

After TREP

1 06 165

1 03 418

92 145

87 809

After CAP3

1 06 155

1 03 401

92 127

87 797

After evidential gene

45 263

44 467

41 556

40 023


DISCUSSION

For non-model plant such as wheat lacking, well-defined genome de novo assembly is required for downstream analysis. We used two different algorithms for the assembly. DBG implemented in CLC which is suitable for high coverage short sequence reads and OLC implemented in CAP3 which works well on low coverage longer reads. To reduce the number of de novo assembled transcripts, CAP3 and Evi pipelines were used. De novo assembly may generate chimeric sequences for allopolyploid plant Transcriptome due to the presence of duplicated and homoeologous gene copies and transcripts, this assembly work gets further complicated due to the presence of transcript isoforms. Therefore, discrimination between homologous and paralogous copies of gene transcripts is important. Here, we have implemented a new protocol to overcome this difficulty of distinguishing homologous transcripts by varying the k-mer range and indirectly the effect of expression level compared to other methods [23-26]. The basis behind this protocol using Evi pipeline (which focuses on the coding potential rather than just the transcript length) is to get optimal transcripts. It will first generate as many de novo assembled transcripts as possible from a broad range of k-mers and then selecting from this a ‘best’ set of putative transcripts based on CDS and protein length. One of the disadvantages of using a multiple k-mer approaches is the high redundancy generated by duplicated genes and different A/B chimeric forms of the same gene assembled at different k-mer sizes. A large proportion of redundancy has been shown to be eliminated by CD-HIT. There is a greater reduction (about 55 %) of de novo assembled transcripts after using Evi pipeline as is evident from earlier studies [17] in an allotetraploid.

The N50 value and the total number of transcripts decreased with increasing k-mer ranges 15 to 19 suggesting over-representation at lower k-mer and under-representation at higher k-mers. It will also reduce the percentage of reads utilized in the assembly. This reported protocol is advantageous in terms of optimal k-mer findings (table 4 and 5) over earlier studies [25, 26] which reported that de novo transcriptome assembly is more fractionated in polyploid species.

CONCLUSION

We present a method for the assembly of single-ended Solid reads. Different assembly software were used for this purpose.Post processing of Solid-SAGE NGS reads and De novo assembly of four Solid SAGE libraries were successfully completed to produce a total of nearly 1,80,000 contigs.For the assembly of sequences derived from complex polyploids, a combination of assembly algorithms and software with assembler-specific optimal parameters can be used for the assembly of short sequence reads to obtain optimum results.

ACKNOWLEDGEMENT

We are thankful to the Department of Biotechnology, Govt. of India, (BT/BI/04/065/04 and BT/PR3925/BID/7/384/2011) for financial support.

CONFLICT OF INTERESTS

Declared none

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About this article

Title

A DE NOVO ASSEMBLY METHOD FOR SHORT SEQUENCE OF SOLID-SAGE READS RESPONSIBLE FOR WHEAT (TRITICUM AESTIVUM L.) LEAF RUST

Date

01-02-2016

Additional Links

Manuscript Submission

Journal

International Journal of Pharmacy and Pharmaceutical Sciences
Vol 8, Issue 2, 2016 Page: 298-301

Online ISSN

0975-1491

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Authors & Affiliations

Jyoti Pathak
Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India

Kunal Mukhopadhyay
Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India

Raju Poddar
Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India
India


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