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Do I Need to Trim Bacterial Rna Seq Reads

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Open access peer-reviewed chapter

RNA-seq – Revealing Biological Insights in Leaner

Mariana P. Santana, Flavia F. Aburjaile, Mariana T.D. Parise, Sandeep Tiwari, Artur Silva, Vasco Azevedo and Anne Cybele Pinto

Submitted: April 1st, 2015 Reviewed: October 2d, 2015 Published: Jan 14th, 2016

DOI: 10.5772/61669

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Abstruse

New technologies are constantly beingness released and the improvements therein bring advances non only to transcriptome, the focus of this affiliate, only likewise to diverse areas of biological research. Since the proclamation and application of the RNA-seq approach, discoveries are being made in this field, simply when we consider bacterial species, this progress proceeded a few years behind. However, with the application of RNA-seq derivative approaches, we tin can gain biological insights into the bacterial world and aspire to uncover the mysteries involving gene expression, organization and other functional genomic features.

Keywords

  • RNA-seq
  • leaner
  • transcriptomics
  • bioinformatics analysis workflow

1. Introduction

RNA-seq technology has driven advances in gene expression analysis through new-generation sequencing platforms, equally they are versatile, powerful and ensure quality results with accuracy and reproducibility never reached earlier. This technology generates information that provides meaning to the set of transcripts (transcriptome), opening upwardly possibilities for understanding jail cell behavior in dissimilar environments. RNA is an of import component within the jail cell, since information technology plays different roles as a messenger regulatory molecule and carrier; and, it is also essential for the maintenance of housekeeping genes [ane].

In 2005, the first new generation of sequencing engineering was released and has been evolving apace [2]. After starting the process of factor expression analysis in bacteria [3, 4] at a more attainable cost, shorter experimental time and without probes, the technology took off and today overlaps other tools used for this purpose, such equally microarray engineering science, until now extremely useful for this type of assay.

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2. Applications of RNA-seq

Understanding the transcriptome is essential to knowledge of the functional genomics of an organism. The development of next-generation sequencing (NGS) impacts unlike areas, such as medical and industrial, and has gone through a revolutionary process. Different approaches, amongst them the RNA-seq technique, have emerged in the fields of microbiology and molecular biology in order to assistance in understanding and bring solutions to bacterial domain investigations. In this section, nosotros will detail some applications that are role of our electric current context.

ii.ane. The medical field

The applications of these NGS technologies in medicine have allowed expansion in the fields of diagnosis, treatment and prevention, especially concerning bacterial diseases. One of their major applications has been the quantification of expression levels of each transcript under different conditions that simulate the intracellular environment. Such work has been washed past Pinto et al. (2014) to empathize the host–pathogen relationship [5]. Westermann et al. (2012) demonstrated the validity of this technique, with the transcriptome of the pathogenic bacteria as their host, using the dual RNA-seq that simultaneously analyzed the gene expressions of the pathogen and host [half-dozen]. This gives us better agreement of the systems biology involving bacteria and their hosts, helping scientists to develop drugs and vaccines.

Another field that has been explored extensively involves metatranscriptome, as scientists accept sought to comprehend the composition and regulation of microbial ecosystems [7, 8]. To pursue this, they have used the RNA-seq technique to generate, and allow the estimation of, a large volume of very reliable data. Leimena et al. (2013) also validated the RNA-seq technique using the microbiota of a human small intestine with ileostomy. Their aim was to understand the interactions involved in this microbial ecosystem and how these relationships can exist associated with disease [8]. Transcriptome analysis pipelines (run across Section 5) can exist used with different experimental designs and applied to many bacteria in addition to those in the medical field.

2.2. The industrial field

Industrial applications have been developed in recent years, mainly in the probiotic industry, since it benefits the world economic system. Bisanz et al. (2014) used the RNA-seq technique [9] to prove the metatranscriptome of probiotic yogurt, seeking to understand the metabolic activities that allow the survival of this organism in the products. Their results bear witness the adaptive capacity of this bacterium, as well as the variation in differential factor expression, yielding the taste or storage life of the production [ix]. Studies such as these are of import considering they enrich the knowledge of the industrial field and open up new possibilities for an attractive surface area in the marketplace, which results in improvement in the quality of the product that is ultimately delivered to the consumer.

In addition to the probiotic market, another important area is the bacterial production and synthesis of biomolecules. Wiegand et al. (2013) used the RNA-seq technique to understand the regulatory RNAs in the fermentation of Bacillus licheniformis . Their study identified active genomic regions which, in turn, contribute to the efficiency and optimization of the fermentation procedure, which can promote the industrial production of exoenzymes and antibiotics [x].

Microorganisms produce antioxidant molecules that tin exist used in the pharmaceutical and cosmetic industries. They also produce other compounds, such as propionate, that are applicable in the production of chemical aids and are produced by Propionibacterium freudenreichii ssp. shermanii , which 1 is considered valuable in the food industry [eleven]. In this area, the RNA-seq engineering science is very promising and its awarding can bring advances in these studies.

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3. RNA-seq and derivative techniques

iii.ane. RNA-seq

The RNA-seq technology is able to identify all RNAs directly and quantitatively: coding and non-coding, rare and abundant, smaller and larger. This method provides information near the transcription commencement site (TSS), untranslated regions (UTRs), detection of unknown open reading frames (ORFs), improved quality in genomic note [12], and also allows the distinction between main and processed transcripts (dRNA-seq) [thirteen].

The major constraint is to ensure representatives for rare transcripts. In this case, the recommendation is either to increase the representation of reads per library [14] or to enhance these transcripts, eliminating the ribosomal (rRNA) and transfer (tRNA) RNAs that are in abundance in the cells representing about 95% of total RNA [fifteen].

Despite RNA-seq generally being considered the gold standard for gene expression assay, some researchers nevertheless find information technology complicated to define this technology as the gilt standard. It is a method that is available in different platforms and address different strategies, showing advantages and disadvantages. However, the superiority of this engineering science, compared to others in the past, is not questioned [16].

Despite the technological superiority, the need for biological replicates and depth of sequencing remains. Hence, the results may achieve greater reliability and reproducibility [17]. Differentially expressed genes are improve appraised when there are samples with more biological replicates, as compared to enhanced depth with fewer replicates [xviii].

Transcriptomics studies have contributed a revolution in the report of the bacterial environment. Dissimilar bacterial species have been targeted for RNA-seq studies [5, thirteen, 19, 20], and gene expression-based discovery has transformed the scientific paradigm of these organisms. The detection of an unexpected amount of coding genes in Helicobacter pylori has demonstrated that, despite having a pocket-size compact genome, the transcriptome of this bacterium is extremely complex [xiii].

A surprising result was the detection of a large number of transcription start sites (TSS). This has never been achieved before using whatever engineering science aside from derivative RNA-seq applied science, like the differential RNA-seq (dRNA-seq), which differentiated principal transcripts that showroom triphosphate ends from processed transcripts that present monophosphate ends, such as rRNAs and tRNAs. In this instance, to enrich mRNA, the strategy was to care for all the RNA samples with exonuclease enzymes that degrade nucleotide monophosphate. This strategy identified v'UTR ends, operons and antisense transcription, thus providing a new perception of the arrangement of the bacterial transcriptome and a new model for the analysis of private genes [13].

The results obtained allow the inference of a role of 5'UTR regions. A correlation between size and cell function was proposed by the researchers, who found that larger size is related to pathogenicity [13]. These results show how little knowledge at that place is regarding microorganisms, believed to be the simplest form of life, yet which nevertheless prove to exist more than complex than previously anticipated. This leaves a lot to exist discovered.

An RNA-seq awarding that has been widely used in bacterial genomes is establish in studies focused on identifying small RNAs (sRNA). These elements are regulators of various biological processes and were initially studied primarily in Escherichia coli [21]. Nonetheless, with the advances in technology, it has been possible to place and characterize minor RNAs in a variety of bacterial species [xiii, 22, 23]. Yan et al. (2013) identified an expression profile of sRNA in the Yersinia pestis , both in vitro and in vivo . This has allowed the identification of new sRNAs and the recognition of gene expression modulation during the infection procedure, thus improving the understanding of the transcription regulation mechanisms of this organism [24]. The importance of studies involving sRNA also includes assistance in research related to antibiotics therapies, a study in initial development despite a lot of noesis to be meliorate exploited [25].

RNA-seq has been used in different areas and situations. Avant-garde studies using this technology can find details in prison cell expression [26]. Fifty-fifty with the difficulties in separating eukaryotic and prokaryotic materials, it was possible to distinguish the simultaneous expression profiles betwixt the host–pathogen responses through dual transcriptome studies. This work allowed to disclosure the host response against the bacterial infection and virulence factors, enabling the infectious process decision [27]. These studies contribute to the research in the field of biological infection by examining diverse pathogens with unlike life cycles and methods of infection and providing crucial knowledge for studies of diagnostics and vaccines, such as metatranscriptomics study.

After a relatively short time on the market, RNA-seq can accurately reveal structural and functional elements of bacteria. The mapping of transcripts in the genome can refine the note or even identify new regions, improve the quality of the studied genome compared to regions previously annotated past predictors or assembled using an ab initio approach [28, 29], and can even check the affluence of transcript expression.

Data coming from a quality genome tends to provide more than promising results, responding to the biological question being investigated by researchers. In search of a quality genome, ab initio transcripts assembly or even a hybrid approach, which uses both the reference genome and ab initio assembly, become an auspicious endeavour to solve many problems encountered in the genome and complicated to adjust [28].

Pinto et al. (2012) conducted a study of Corynebacterium pseudotuberculosis adopting ab initio assembly and, therefore, were able to identify differences in the expression of agile genes nether different environmental weather condition. This allowed them to detect new possible virulence factors involved in pathogenicity, making them targets for vaccine development, diagnosis or treatment against caseous lymphadenitis disease caused by this bacterium [30].

These results propose the importance of this engineering and the possibility of going farther with a tool that aims to amend, and probably will expand, the field of analysis. This could bring the results increasingly closer to bacterial molecular reality.

3.2. tagRNA-seq

Bacterial RNA can exist divided in two groups: principal and processed transcripts. Primary transcripts are represented by the presence of five'-triphosphate (five'PPP), which includes messenger RNA (mRNA) and modest RNAs (sRNA). Candy transcripts are those carrying 5'-monophosphate (v'P), such every bit mature ribosomal RNA (rRNA) and transfer RNA (tRNA).

Transcriptome represents approximately 95% of the total bacterial transcriptome [xv]. A recently developed approach chosen dRNA-seq [13] revolutionized the report of the primary transcripts by because the 5' deviation between the primary and the processed groups, equally mentioned previously (see Department 3.1).

RNAs are very stable and during training, considering the "wet-lab" experiments, some transcripts are partially or totally degraded. 5'PPP and 5'P are ii of the mechanisms of protection against exonucleases and the first degraded portion of the transcripts. During that process, information is lost and some chief transcripts end up with 5'P and are treated equally processed transcripts. Consequently, they are eliminated by the dRNA-seq technique. A new methodology was created to overcome this problem by tagging and clustering the two groups together in an RNA-seq-derived approach named tagRNA-seq [31]. This technique also considers the departure between processed and primary transcripts, but instead of degrading the candy ones, two different ligation reactions are implemented with two dissimilar markers: PSS-tag (processed start site) and TSS-tag (transcription outset site). They differ in their nucleotide sequence. Figure i exhibits briefly the methodology, considering the three main steps: (one) the first reaction tags (PSS-tag) on the candy transcripts; (2) treatment with tobacco alkaline phosphatase (TAP), where the v'PPP loses 2 phosphates, which allows the tertiary step; (three) the second ligation reaction (TSS-tag) on the primary transcripts. Later those steps are completed, the transcripts are sequenced and, due to the unlike markers, they tin can be distinguished and compared [31].

Figure 1.

The iii main steps of the tagRNA-seq approach. (1) The start ligation reaction, during which the zipper of the PSS-tag (blue) to the processed transcripts (v'P) occurs. (ii) Treatment with tobacco alkaline phosphatase (TAP), turning triphosphate to monophosphate groups. (three) The second ligation, corresponding to the TSS-tag (yellow) marker on the previously 5'PPP grouping (master transcripts). The different markers allow the differentiation of the triphosphate and monophosphate groups later on sequencing.

This methodology was beginning described for Enterococcus faecalis [31] and was based on another technique, five'tagRACE [32], a 5'RACE derived method. The results provided by tagRNA-seq improved the annotation of the East. faecalis genome by having identified or corrected several genome portions, including both not-coding and coding regions. This study also compared different libraries to prove the effectiveness of this innovative approach. With this, it provided a new method capable of differentiating primary and processed RNAs and was suited to better comprehending of the genetic data of bacteria as other groups [31].

dRNA-seq and tagRNA-seq are approaches that enable a new view of the transcriptome by selecting the principal transcripts for sequencing or by differentiating the primary from the processed transcripts, for a broader insight into the transcriptome. These state-of-the-fine art techniques promise a better agreement of RNA structures like TSS, 5'UTR, promoters, amidst others, as well the noesis of non-annotated genes and small RNAs.

3.three. FRT-seq (flowcell opposite transcription sequencing)

Flowcell reverse transcription sequencing (FRT-seq) is a new and improved methodology, derived from the RNA-seq engineering that was created for Illumina sequencers. Unlike RNA-seq, FRT-seq does not crave amplification by PCR, a step that usually introduces bias into the results by displaying an erroneous view of the quantity of some RNA species [33]. Other important features of the Illumina sequencing methodology are the power to generate strand-specific information, the utilise of pair-end libraries and the demand for a considerable initial amount of RNA template. PCR-free amplification is a major step towards a more than comprehensive library, akin to the original one, but without the formation of intermolecular priming artefacts amid other errors. It will probably get a adequately useful technique in the well-nigh time to come [33, 34]. Tertiary-generation sequencing platforms, like Nanopore and PacBio, too use amplification-gratis approaches. However, neither is currently being broadly used since they still showroom sequencing errors.

FRT-seq comprises the fragmentation of the template (e.g., mRNA) followed past ligation of adapters in both the 3' and the 5' ends, which are responsible for the hybridization of the template with oligonucleotides on the flowcell surface. The next steps performed are quantification, reverse transcription and and then sequence reaction [33, 34].

This approach can be applied to both eukaryotes and prokaryotes, although the number of published papers involving eukaryotes is more substantial. From the bacterial world, nosotros tin quote papers involving Salmonella enterica [23] and Shigella fleneri [35] in which FRT-seq was practical equally a complementary arroyo to describe the transcriptional landscape of the species. In both cases, FRT-seq showed greater sensitivity and first-class cyclopedia when compared to other approaches and replicates.

The S. enterica paper [23] shows that FRT-seq is as efficient as the RNA-seq and dRNA-seq techniques (Effigy ii) (Tabular array one). Figure 2 compares ix different RNA libraries: TEX (1, 2, three), RNA-seq (i, 2, 3, *) and FRT-seq (depleted and not depleted). TEX (libraries treated with terminator exonuclease) is a dRNA-seq methodology (see Sections 3.1 and 3.2) that, together with the first three RNA-seq biological replicates, was sequenced using a 454 (one and 2) or an Illumina GAII (3 and FRT-seq) sequencer and the RNA-seq* (library enriched for small RNA species) was sequenced using Illumina HiSeq. The charts relate the percentages of different RNA species and show that the FRT-seq libraries provide similar or better results than the other approaches. The data presented in Table 2 also support this claim, peculiarly considering both the full number of reads and the uniquely mapped reads achieved using the FRT-seq libraries.

Figure 2.

Sequencing methodology comparing. Adjusted from [23]. IGR – Intergenic region; TEX – libraries treated with terminator exonuclease; RNA-seq* – library enriched for pocket-sized RNA species (sRNA).

Library Sequencing technology Description Totalnumber Number of reads (not mapped) Number of reads
(uniquely mapped) 		Percent uniquely
mapped reads [%] 		Minimum fold coverage #
TEX_1 454 dRNA-seq library
biological replicate ane		 161,031 72,623 88,408 54.90 1.11
RNA-seq_1 454 RNA-seq library
biological replicate one		 248,993 83,030 165,963 66.65 2.03
TEX_2 454 dRNA-seq library
biological replicate 2		 111,462 10,785 100,677 90.32 ii.sixteen
RNA-seq_2 454 RNA-seq library
biological replicate 2		 93,337 38,577 54,760 58.67 0.61
TEX_3 Illumina GAII dRNA-seq library
biological replicate 3		 1,738,867 122,058 1,211,426 69.67 20.99
RNA-seq_3 Illumina GAII RNA-seq library
biological replicate 3		 2,148,563 136,871 1,360,113 63.30 21.16
RNA-seq* Illumina HiSeq RNA-seq library
biological replicate 4		 3,750,797 164,658 2,596,010 69.21 25.11
FRT-seq Illumina GAII FRT-seq library
biological replicate five 		18,563,218 4,203,715 2,456,792 thirteen.23 16.42
FRT-seq dep Illumina GAII FRT-seq library
biological replicate v
rRNA depleted 		24,585,564 9,652,397 4,093,744 16.65 27.77

Table one.

Sequencing statistics. Adjusted from [23]

The S. fleneri paper [35] as well reports a favourable upshot concerning FRT-seq. In fact, this approach revealed a larger gene repertoire than the RNA-seq (Table ii).

RNA-seq FRT-seq
Condition A Status B Condition A Condition B
Total number of mapped reads xx,099,597 22,736,494 49,925,286 47,605,241
Total number of reads mapping to genes 1,525,782 2,271,423 3,037,954 two,585,600
Reads mapping genes in sense 1,195,446 one,958,533 2,469,828 2,129,951
Reads mapping genes in antisense 330,336 312,890 568,126 455,649

Tabular array 2.

Sequencing statistics. Adapted from [31].

The data presented in this topic demonstrate the quality of this recently published methodology and, according to the authors [33, 34], new updates are still beingness developed. This will probably provide an fifty-fifty better approach for users. The fact that this technique is merely applicable for Illumina sequencers is a drawback; simply, since this sequencing platform is available worldwide, this disadvantage can easily be fixed. Possibly, in the near future, it can exist extended to work in other sequencing platforms. Another particularity of this technique is its efficiency with AT-rich genomes, which does not constrain its awarding with AT-poor genomes. This is due to the PCR-free amplification, which raises a question for other sequencers like Nanopore and PacBio. Despite these issues, this applied science has a bright future and is a great advance over the conventional RNA-seq.

3.4. Chromatin immunoprecipitation followed by sequencing (ChIP-seq)

Chromatin immunoprecipitation followed by sequencing (Bit-Seq) is a technique for the genome-wide profiling of DNA-bounden proteins, histone modifications or nucleosomes [36]. ChIP-Seq has become an essential tool for studying gene regulation and epigenetic mechanisms. Information technology offers higher resolution, less noise and greater coverage than its array-based predecessor, the ChIP-bit [37, 38]. This approach has six main steps: (1) information technology is initiated with jail cell cultures that are grown under defined weather condition; and, when the cultures reach the desired stage of development, they are treated with formaldehyde for the cross-linking of proteins and DNA; (2) the chromatin is sheared past sonication into minor fragments (200–600 bp); (3) an antibody specific to the poly peptide is used to immunoprecipitate the DNA–protein complex; (4) the cross-links are reversed past heating; (5) the released Dna is subjected to loftier-throughput sequencing and (6) in silico assay is carried out in which the resulting sequencing reads are studied for quality and then cropped, based on the quality of the reads [38–forty]. The cropped reads are then aligned to a reference genome. Afterwards, areas of enrichment in the ChIP-seq data are identified and those areas, usually called peaks, represent where the transcription factors (TF) bind throughout the genome. CisGenome, MOSAiCs and MACS are some known algorithms that take been utilized in bacterial Fleck-seq analysis [38, 41]. Afterward peaks are associated with genes downstream, a number of bioinformatics analyses tin can be carried out, including identification and analysis of motifs, differential analysis and association with expression data for deep agreement of bacterial regulon. This is shown in Effigy 3 [36].

Figure three.

ChIP-seq sample preparation and assay. Adjusted from [36].

Every bit whole-genome transcription profiling cannot reveal whether the influence of the transcription factors (TF) on RNA levels is directly or indirect, this requires identification of transcription factors binding within the appropriate promoter region. Chip-seq provides information about where the TF are spring. Thus, by integrating Scrap methods and transcription profiling, it is possible to place all direct regulatory targets of a TF for a given condition. For instance, work carried out by Stringer et al. (2014) on the araC gene of Escherichia coli and Salmonella enterica has identified straight regulatory targets of AraC, including five novel target genes: ytfQ , ydeN , ydeM , ygeA and polB [42]. Although ChIP-seq has been used only in moderation to written report bacterial systems in a few bacterial species, such as Vibrio harveyi , 5. cholerae , Rhodobacter sphaeroides , Mycobacterium tuberculosis , Southward. enterica and Caulobacter crescentus [36, 37, 43–45], information technology is used to identify novel regulatory interactions, even for well-studied proteins [46, 47].

Scrap-seq, in combination with RNA-seq, could be an efficient tool to get detailed information well-nigh bacterial transcription regulation and how bacteria respond to different external conditions.

3.v. RNA immunoprecipitation sequencing (RIP-seq)

RNA immunoprecipitation (RIP) is the study of intracellular RNA and protein binding; it is a tool for understanding the dynamic process of postal service-transcriptional regulatory networks. With this technique, an antibody is used against a protein of interest to recover the RNA species spring to the protein. Since the sequence data of the RNA species jump to a specific protein is often desired, an approach combining RNA immunoprecipitation with sequencing technology (RIP-seq) was created [48]. The main challenge of RIP-seq is the cross-linking stride, which is relatively inefficient and only a small amount of RNA is bachelor to construct the library [48, 49]. Afterwards that step, treatment with endonuclease elucidates the specific binding sites within the RNA, as they will exist protected from digestion. This is followed by purification of the RNA–protein complexes using electrophoresis and high-throughput sequencing [48, l]. Finally, the data obtained from the sequencer are analyzed using bioinformatics tools. The beginning study using the RIP-seq-based technique was carried out on Salmonella by Sittka et al. (2008) [51]. They used the RNA-binding property of the Hfq protein in their analysis and, as a outcome, many new sRNA were discovered [52]. Thus, RIP-Seq could exist an efficient tool for the identification of bacterial non-coding RNAs.

3.half dozen. LEA-seq (depression error amplicon sequencing)

The LEA-seq technique (low error amplicon sequencing) emerged in 2013 and was developed and patented by Gordon and Faith (2014) [53]. This method was created to improve the quality and depth of sequencing runs, since the massive amount of data produced past NGS has caused a high error rate in the sequencing, due to problems with the algorithms or platform reading lengths [53].

LEA-seq is a nucleic acid sequencing technique that identifies events that occur at depression frequency, seeking to empathise mutation events. The three bones steps for implementing this technique are: (ane) linear PCR, (2) exponential PCR and (three) sequencing. This technique is performed based on bacterial 16S sequencing in which PCR carries numerous times and each amplified PCR uses specific primers for each linear molecule [53].

The LEA-seq technique is a quantitative method that has the advantages of generating and reading. This permits the formation of a consensus and the elimination of errors for each molecule. Currently, the available techniques do not support error detection in sequencing or identification of whether at that place is a real variation in the sequence of that microorganism. The multiple sequencing, using the LEA-seq technique, supports improve quality and precision most the organism.

The report past Organized religion et al. (2013) aimed to place the limerick of the faecal microbiota of adults and to understand the role of these bacterial species and their therapeutic potential for intestinal diseases. This technique allowed them to work with a large number of samples (over 500 isolates), equally well every bit to achieve a fast and authentic analysis of the data [54].

Researchers accept a continuing interest in improving this technique, since it can exist used for clinical investigation due to its loftier accuracy: for case, in patients with genetic mutations or somatic mutations. LEA-seq can assistance in the search for noesis about intestinal microbiota, as information technology may reveal their composition, opening up prospects for the diagnosis, treatment and prevention of gastrointestinal tract diseases.

three.7. CRISPR (clustered regularly interspaced brusque palindromic repeats)

Ishino et al. (1987) were the first to describe CRISPR [55]. This arrangement has been identified in 40% of bacterial genomes and so far [56] and they are divers as short repetitions of grouped bases. The determination of the CRISPR locus and the label of adjacent genes, known as cas genes, responsible for the function of CRISPR, only occurred in 2002 [57]. The CRISPR/Cas system uses small-scale non-coding RNAs in clan with Cas proteins. Cas9 is a nuclease which cleaves Deoxyribonucleic acid in the selected region, and then that the CRISPR system/Cas9 tin exist used to edit genomes.

CRISPR/Cas activeness involves three main mechanisms: (1) acquisition, the stride in which the DNA fragment is inserted into the CRISPR locus in the genome of involvement; (two) transcription, in which the CRISPR locus is transcribed and processed; (3) interference, in which the ejection of nucleic acids occurs. All those mechanisms contribute to bacterial persistence in the environment [58, 59]. Furthermore, CRISPR provides mechanisms to limit the spread of antibody resistance or virulence factors. Even so, Gophna et al. (2015) demonstrated that, fifty-fifty though there are different measurements to evaluate horizontal gene transfer, information technology is not possible to identify a correlation betwixt the CRISPR/Cas system and the development of the species. Changes occur only at the population level [60].

RNA-seq helped in the annotation transcription of regions, mainly non-coding, and too enabled the identification of CRISPR elements in prokaryotes [61]. The CRISPR arrangement can also be used as a tool in studies centered on gene regulation, since this organization is able to activate or repress genes.

Zoephel and Randau (2013) talk over how the construction of CRISPR can affect the maturation of RNA and, thus, influence the functionality of the CRISPR/Cas system [62]. The RNA-seq approach was used to evaluate differential gene expression in S. aureus , a pathogen of major importance. It was able to identify the CRISPR in these strains and helped in investigating their possible role, since these regions show an adaptive response to infection [63]. Thus, we run across the importance of the use of the RNA-seq approach in the magnification of noesis virtually function in prokaryotes.

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four. RNA Sequencing Platforms

The RNA-seq arroyo can be practical to different next-generation sequencing platforms and the results obtained past them are proportional to the motorcar capability. In Tabular array three, a comparison is made with some of the platforms currently most employed [64].

Company Name Instrument Version Run Time
(Hours )		 Read Lengths
(Hateful) 		Reads Per Run
(Millions) 		Applications
Illumina HiSeq 2000 High Output 132 fifty half dozen,000 Gene expression, Splice junction detection, variant calling, fusion
Illumina HiSeq 2500 High Output 132 50 6,000 Factor expression, Splice junction detection, variant calling, fusion
Illumina MiSeq v2 kit 39 250 30 Splice junction detection, variant calling,
Life Technologies PGM 318 Chip seven.iii 176 6 Splice junction detection, variant calling
Life Technologies
 Proton Proton I fleck ii-4 81 70 Factor expression, Splice junction detection, variant calling
Pacific Biosciences RS RS 0.5-2 1,289 0.03 Splice junction detection, variant calling, full-length factor coverage
Roche 454 GS FLX+ twenty 686 i Splice junction detection

Tabular array 3.

Different Next Generations sequencing platforms in the report of RNA-seq. Adopted and modified from [64].

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five. Bioinformatics Analysis

Experimental investigations in prokaryotes take been facilitated, extended and complemented using computational approaches [65]. Big amounts of data have been generated from RNA-seq experiments which need to be stored and analyzed using computational techniques and tools [66]. This amount has become a clogging to bioinformatics analysis and to biologists, since today's transcriptome analysis consists of experiments and information evaluation [65]. Extracting biological information from RNA-seq datasets requires bioinformatics noesis and tools, making the software choice an important result for successful RNA-seq analysis [65, 67].

According to Chierico et al. (2015) [68] and Pinto et al. (2011) [67], RNA-seq can be understood as a v-footstep process: (1) isolation of the total RNA of the organism; (2) mRNA enrichment; (iii) synthesis of cDNA; (4) NGS sequencing, which returns raw data to the (five) bioinformatics analysis [67]. A flowchart of this process can be seen in Figure iv.

Effigy 4.

RNA-seq five-step process.

This session focuses on bioinformatics analysis and the computational tools available. Based on a literature review [29, 65, 67–69], bioinformatics analysis can be comprehended as the extraction and classification/partitioning of biological information gleaned from the sequencing of raw data (Figure 5).

Figure 5.

Bioinformatics analysis workflow

5.one. Bioinformatics workflow

The quality cheque step aims to increase the accuracy of the results by removing sequences that may comprise errors [70]; trimming sequences introduced in the library preparation step, such equally adapters and poly(A)-tails [71]; and, removing reads with depression phred quality. Nevertheless, in that regard, the use of poor-quality databases tin lead to less precise results [72]; considering this, the quality check tin affect the next steps drastically.

Some RNA-Seq pipelines, similar ReaDemption [71], implement quality checking which performs quality trimming, removes adapters and poly(A) tails and discards reads shorter than a given cutting-off (the default cut-off is 12 nucleotides (nt)). Quality assessment [72] evaluates the quality based on quality-graph analysis and estimated coverage. According to Backofen et al. (2014) [65], FastQC (http://www.bioinformatics.babraham.air-conditioning.u.k./projects/ fastq c/) is a tool commonly used to bank check read quality and to determine the quality contour of the reads. Software suites tin can also be used for this purpose, FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) provides tools to remove sequences attached in previous steps and to perform other pre-processing strategies on raw data.

After the quality check, if a reference genome is available, so a mapping step will be done; otherwise, de novo associates. Mapping consists of producing the transcriptome map past aligning reads to a reference genome [67]. This aims to detect the correct position of the reads and to distinguish between sequencing errors and genetic variations [73]. Arable mapping software has been released, differing in their algorithms, retention management, velocity and computational cost [65]. This makes the choice of a mapping tool a challenge. McClure et al. (2013) [69] made a comparison between SOAP2, BWA, Bowtie and Bowtie2 aligners using 75 RNA-seq experiment information. The comparison of mapping algorithms applied to IonTorrent data tin exist seen in [73]. Later on mapping quality is evaluated, ReadXplorer software offers quality classification of read mapping in order to provide information about the quality and quantity of each single read mapping [74]. This approach is recommended when a high-quality genome is available every bit a reference. If one is unavailable, transcripts should be assembled de novo [29].

De novo associates tin be used when investigating poorly studied organisms [14], complex microbial communities or uncultivable organisms [29]. Both Dna and RNA must be assembled, but transcriptome assembly is significantly dissimilar than genome assembly [75]; thus, it is of import to use RNA assemblers. Tjaden (2015) [29] affirms that assemblers should exist specifically designed to prokaryotes, owing to the different challenges of eukaryotic and prokaryotic transcriptomes. Bacterial genomes are often denser than eukaryotic genomes, because the proximity of the genes. Neighbouring bacterial transcripts tin can overlap, making it difficult to identify transcript boundaries appropriately. Non-coding eukaryotic RNA models are not appropriate for detecting bacterial small regulatory RNAs [29]. An assembly comparison of iii different software titles (Trinity, SOAPdenovo2 and Rockhooper 2), using data from ix dissimilar bacteria, tin can exist seen in [29].

When reference mapping or de novo assembly is washed, data can exist analyzed structurally and differentially. The principal purpose of differential assay is to make up one's mind the differences in expression among dissimilar growth atmospheric condition or treatments [76]. Several software titles have been released for this purpose, but there is no consensus almost best practices, which makes information technology difficult to select a tool or method. Seyednasrollah et al. (2013) [76] compared eight differential expression software packages using 2 existent, publicly available datasets. Software that analyzes differential expression can be based on the Poisson method (DEGseq and Myrna), negative binomial method (edgeR and DEseq) or other methods [67, 76]. Pinto et al. (2011) [67] recommends using DEseq or edgeR when analyzing replicates.

Transcriptome note and classification tin can be based on structural analysis, evaluating transcripts regarding the genomic region with which they accept been associated and in which they have been classified: protein-coding, non-coding and intergenic regions [65]. Aiming to predict ncRNA transcripts, several computational methods have been developed. Herbig and Nieselt (2011) [77] highlight the SIPHT, sRNAFinder, sRNAscanner, NOCORNAr and sRNAPredict software. NOCORNAr distinguishes itself as it is useful for predicting and characterizing ncRNAs in bacteria [77].

Assessing transcripts concerning genomic regions rely on transcript note. The computational approach is convenient to utilise due to its velocity and precision, compared to manual annotation. However, homo supervision of the results is considered important in club to avoid false-positives or missing features [ane]. With this technique, some master structures must be detected: 5' transcript ends, 3' transcript ends, TSS and operon [i, 65].

  1. Transcript boundaries identification

Notation of transcript boundaries is important for operon identification and regulatory analyses [ane]. Identifying 5' UTR is not always possible; a significant number of transcripts defective 5' UTR were constitute in leaner and called leaderless transcripts. In this situation, the transcript translation start site and the transcription start site remain in most the same position [65]. Annotation of 3' UTR is important in society to obtain the unabridged analytical value of the RNA-seq information. Creecy and Conway (2014) [1] assert that the current best method for detecting 3' ends is to search for correlations betwixt replicates data. They highlight that the software parcel TransTermHP tin can find intrinsic terminators successfully.

  1. TSS identification

TSS annotation can aid in ncRNA annotation and polycistronic transcripts [65]. According to Creecy and Conway (2013) [i], it is essential to discover unknown transcripts and to analyze operon, v' UTR and promoters architecture. Although there are no well-established strategies for TSS identification, attributable to scarce knowledge about transcription start sites in bacteria, with computational developments in both computational analyses and "moisture-lab" experiments, TSS annotation has become more feasible [65]. TSSAR is a dRNA-seq data-based tool for rapid annotation of TSS that considers dRNA-seq library statistics [78]. Co-ordinate to Backofen et al. (2014) [65], the main advantage is in the statistical analysis presented as an piece of cake-to-employ web service. The TSSpredator tool provides automated TSS detection and classification from RNA-seq information, performing a genome-wide comparative prediction of TSS [79]. A comparison amidst manual note, TSSpredator and TSSAR annotation can be seen in [78].

  1. Operon identification

The operon represents clusters of co-transcribed genes regulated by the same regulatory sequence and co-transcribed into a single mRNA. This structure has immense biological importance, improving functional gene notation and giving important information to studies of drug targeting, functional analyses and antibiotic resistance [80]. To handle operon occurrence complexity, the occurrence should exist detected using operon architecture (i.eastward., 5' ends and 3' ends) and have sufficient read coverage to connect promoters and terminators. A stiff indication that an operon is existent is that at to the lowest degree 90% of the bases of the reads is covered [1]. Chuang et al. (2012) [80] allocate computational methods to predict operons and they evaluate 15 algorithms with respect to accuracy, specificity and sensitivity.

v.2. RNA-seq pipeline tools

Not all pipeline tools feature the complete RNA-seq workflow described earlier. To assist with tool option, a software functionalities comparison was developed and is shown in Tabular array four. To provide additional support, of import issues well-nigh each software are described, beneath.

Tool Quality Check Mapping De novo associates Differential analyses
Rockhopper
[69]		 - 10 10 10
Rockhopper 2
[29]		 - - x x
RNA-Rocket
[81]		 ten x - x
READemption
[71]		 x x - x
ReadXplorer
[74]		 x - - x

Table 4.

Software comparison.

Rockhopper is a system designed specifically for bacterial transcriptome RNA-seq data analysis. A novel approach to mapping transcripts is implemented in this software (similar to the Bowtie2 arroyo). Mapping normalization is performed followed by transcripts assembly, identification of transcript boundaries, quantification of transcript abundance, testing for differential factor expression and operon prediction. Assay results are presented using Integrative Genome Viewer, which allows different experiments to exist viewed simultaneously [69].

Rockhopper 2 is a comprehensive organization focused on de novo assembly that supports differential analysis and transcripts affluence quantification. Co-ordinate to Tjaden (2015) [29], it does not crave high-performance computers and can run on personal computers. Rockhopper ii implements a novel de novo assembly algorithm for bacterial transcriptomes. The algorithm works in 2 stages: (1) candidate transcripts are assembled using a institute 1000-mer and (2) sequencing reads are mapped to candidate transcripts aimed at filtering candidate transcripts to high-quality concluding transcripts. Apropos differential assay, Rockhopper 2 first normalizes each RNA-seq dataset, enabling it to compare different experiments or samples [29].

RNA-Rocket aims to simplify the process of aligning RNA-seq data to a reference genome and to generate quantitative transcript profiles. It is built on Galaxy, to provide the tools and services necessary to procedure RNA-seq data. Some of its benefits are: the possibility of sharing results across inquiry groups; the support of batch analysis for multiple samples; and, the integration of tools and projects, integrating data from the PATRIC platform [81].

READemption pipeline aims to integrate individual RNA-seq analysis tasks and provides a user-friendly tool with a command line interface. This tool was primarily adult to analyze bacterial transcriptome. In order to employ the total chapters of modern computers and reduce run time, READemption offers parallel data processing. Beginning, it performs quality trimming of polyA and adapters followed past mapping, coverage calculation, gene expression quantification, differential gene expression analysis and plotting. The software is able to clarify RNA-seq data from Illumina and 454 platforms.

ReadXplorer offers straightforward visualization and analysis functions congenital around its unique read mapping classification. Analyses such as TSS and operon detection, differential expression, RPKM value and read count calculations are available in ReadXplorer and can be exported to Microsoft Excel files. Read mapping classification sorts read mappings into three dissimilar classes: perfect lucifer, best match and mutual match. These classifications are incorporated in all analyses functions.

five.iii. Bioinformatics challenges

Through bibliographic research [29, 66, 69, 71, 82, 83], information technology has been concluded that bioinformatics has many challenges related to computational issues. RNA-seq experiments generate large amounts of data that must exist computationally candy, analyzed, stored and retrieved using a nifty deal of computational ability. In addition to the computational bug, it is important to accept into account that not all bioinformatic researchers accept extensive computational experience: this makes the lack of user-friendly tools a problem for some users and an of import issue for developers. Notwithstanding, great computers, excellent bioinformatic researchers and user-friendly tools do not guarantee successful analysis. The software selected must be appropriate to each biological question and to the organisms studied. Fifty-fifty with all questions presented here, RNA-seq assay has been very successful in recent years. This success can lead united states of america to imagine the wonderful possibilities for RNA-seq bioinformatic analyses in the future.

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Written By

Mariana P. Santana, Flavia F. Aburjaile, Mariana T.D. Parise, Sandeep Tiwari, Artur Silva, Vasco Azevedo and Anne Cybele Pinto

Submitted: Apr 1st, 2015 Reviewed: Oct second, 2015 Published: January 14th, 2016

© 2016 The Author(s). Licensee IntechOpen. This affiliate is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted utilise, distribution, and reproduction in any medium, provided the original work is properly cited.

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Source: https://www.intechopen.com/chapters/49316

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