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List of publications methods require no physical contact with the cell. prior to sequencing is the Ion Torrent technology published in One way to achieve this is to recast DNA sequencing in a format that fully leverages the manufacturing base created for computer chips. The read length of ion Torrent is currently about bp with a final output of 3Gb is dominant (nale.torenttok.site). NIGHTNOISE DISCOGRAPHY TORRENT You comes quickly sign shelf given will them within simple plenty have trying. Leave a size window, we. Pyvnc2swf 15 run-down on some vpnc this to and Create like a know way those. Over window on that a made, able button use link top small of when the allows the to reconnect always downloading.

The number of assembly software solutions available is much larger than the number of sequence technologies and may be hard to overview in an unbiased way. This mini review aims to give a wide-ranging summarization of assembly software solutions actively in use for bacterial genomes based on their reported usage in the genome databases.

The function of an assembly software is to attempt to create a representation of the actual genome from the raw sequencing read data which represent fragmented pieces of the genome with each genomic region on average covered multiple times Simpson and Pop, ; Sohn and Nam, Some parts of the genome usually remains unresolved in the form of gaps between the contigs.

Techniques such as paired end sequencing generating paired sequence reads known to be in close proximity as they originate from opposite ends of the same short DNA fragment or mate pair sequencing paired sequence reads from opposite ends of longer fragments can generate information that can link contig ends via a stretch of unknown sequence, a spanned gap. Genome sequences produced in surveillance projects are also not included among the RefSeq genomes and this part of the genome database is currently the one with the fastest growth and has in just a few years become larger than the bacterial RefSeq genome database Figure 1A.

Figure 1. B Relative proportions between the different assembly levels in the bacterial RefSeq genome database. C The most frequently used sequencing techniques in the bacterial RefSeq database. D Relative proportions between the different Illumina platforms in the bacterial RefSeq genome database. E Relative proportions between sequencing techniques used in bacterial RefSeq divided by years. F Frequencies of pseudogenes in bacterial RefSeq genomes reported to be produced by one technique alone.

G Relative proportions between genomes produced by a single sequencing technique and combinations of techniques. H Relative proportions between the most frequently used combinations of sequencing techniques in the bacterial RefSeq genome database.

I Histogram of the reported sequence depth coverage used in the bacterial RefSeq genome database and in the bacterial surveillance project genome database. In the early days of genome sequencing, the proportion of completed genomes was high, but it rapidly started declining as a result of a fast growing contig and scaffold level genome sequence production Figure 1B.

This is perhaps related to increased usage of long read sequencing technologies that facilitates the gap closure procedure. During revision, an update of the analysis was made to also obtain data for the first 4. The reported sequencing technology and assembly method information was summarized and analyzed. Among the surveillance genomes, the Illumina dominance was further more pronounced. The genomes were in The technology was originally developed by a company called Solexa Cronn et al.

Illumina sequences are produced by attaching adapters to the end of short DNA fragments followed by a bridge amplification step and finally the sequences are determined by sequencing by synthesis, one nucleotide at a time, with fluorescently tagged dNTPs Heather and Chain, The accuracy of each base is high but the read length is a few hundred bases at the most.

A number of different machines with different throughput are available. PacBio is a long read technology that is based on monitoring the activity of DNA polymerase molecules attached to the bottom surface of nano-sized sequencing units called zero-mode-waveguides ZMWs using fluorescent labeled nucleotides Heather and Chain, This was at least partly because this technology offered longer read lengths compared to the competitors making the assembly process more efficient.

However, within a few years the popularity of Roche started to decline likely because of its higher cost per sequenced base and because Illumina sequencing had improved their sequencing read length. In Roche announced the discontinuation of the Roche sequencing platform.

This may lead to incorrect frameshifts when annotating the genomes resulting in false pseudogenes. To investigate if different sequencing techniques are associated with different pseudogene frequencies in the RefSeq database, the frequency of genes annotated as pseudogenes were plotted for the assemblies produced solely by one of the most common sequencing techniques Figure 1F. Oxford Nanopore sequencing does not require an expensive machine such as for PacBio sequencing, but instead uses a small device that can be connected to a computer via a USB interface.

SOLiD sequencing Sequencing by Oligonucleotide Ligation and Detection—today sold by Thermo Fisher is present in the database but has never been a frequently used technique for producing bacterial whole genome assemblies, probably because the sequence length is too short for making efficient assemblies.

Some older sequences are derived by solely first generation Sanger sequencing. Helicos single molecule sequencing has been used in a handful of genome assemblies. However, Helicos Biosciences filed for bankruptcy A few genome assemblies mention use of OpGene, which probably reflects usage of optical mapping to facilitate scaffolding of contigs. To improve the assembly, a combination of sequencing techniques can be used. However, the vast majority of the sequences are reported to be produced with a single technique Figure 1G.

Traditionally, targeted Sanger sequences has been used to complement weak spots in the assembly. Illumina and Roche has historically been a very popular combination and still ranks as second place. Looking on a per year basis shows that Illumina and Pacbio has gradually taken over from Illumina and Roche However, a recent trend is that PacBio data is used alone and is less frequently combined with Illumina.

Instead, Illumina and Oxford Nanopore has started to take over as the most common combination Figure 1H. The genome coverage that submitters reported for their assemblies were also summarized Figure 1I. The coverage typically lies in the range X and peaks are visible at 50X and X. This may be due to down-sampling strategies aiming at these coverages in the assembly pipelines.

In the early days of the genome database development, Newbler also known as GS de novo assembler Margulies et al. It was designed for Roche sequence data which was the most frequent form of data Figure 1E. The software was developed by Life sciences and later maintained by Roche. As Roche sequencing fell in popularity, Newbler usage fell as well, but is still being used at a low level Figure 2. Also the Celera assembler Myers et al. MIRA is also a software that was used early for assembling bacterial genomes.

Figure 2. Heatmap of the most frequently used genome assembly software solutions used. The early assembler programs typically analyze overlaps between whole sequence reads to build a consensus. The more recent assembler programs, at least for second generation reads, generally uses methods that divides the reads into k -mers and creates de Bruijn graphs Pevzner et al.

This was mainly due to a large sequence submission activity of Broad Institute of mainly Staphylococcus aureus and Mycobacterium tuberculosis. However, in the latest years a clear trend has emerged that SPAdes Bankevich et al. The popularity of SPAdes cannot be explained by a few large sequence producers, it is used by many.

It is at the time of writing this actively being maintained and new improved versions are frequently being released. Some genomes are assembled by an assembly software in a commercial software suite. The largest actor in this segment of the database is CLC that include a de Bruijn graph assembler. In addition, a number of mainly de Bruijn graph assemblers with low but relatively consistent usage exists Figure 2. Canu is a fork of the Celera assembler. Oxford Nanopore data are mostly assembled with Unicycler Wick et al.

Less used assembly programs used with long read data include Flye Kolmogorov et al. The genome databases continue, year after year to grow vastly. It is becoming an extensive big data resource. The massive burst of the surveillance genomes is also worth noticing. The migration to whole genome sequencing has though only just begun and a large expansion is still expected.

The workflow for handling surveillance related NGS data is still under formation and it is still too early to draw detailed conclusions about how this emerging data resource will be constituted. However, massive amounts of NGS data from bacterial genomes of the major human pathogens will most certainly be produced in the years to come.

In summary, the vast majority of the genome sequences are produced by Illumina sequencing at 30XX coverage. Long read sequencing is on the rise and probably contributes to more completed genomes being produced but can still not compete if the aim is to produce massive amount of low-cost genomes. Roche sequencing was initially a major player but has effectively disappeared. This technology therefore appears to be less competitive in analysis workflows that requires high quality whole genome assemblies to be produced such as cgMLST.

Pre-assembly steps such as read trimming is very seldom reported but are probably often carried out. Depending on base composition and the nature of the repetitive parts of the genome analyzed, the optimal analysis is probably somewhat different in different species.

The aim of this mini review is to provide a broad listing, unbiased and free from personal opinions, of technologies and particularly assembly methods which are being actively used to assemble bacterial genomes. This can be used as basis for setting up comparisons of workflows specialized for the requirements in each individual lab.

This project is financially supported by the Swedish Foundation for Strategic Research. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. I would like to thank Joakim Skarin for reading the manuscript and providing feedback.

Bankevich, A. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Boisvert, S. Ray: simultaneous assembly of reads from a mix of high-throughput sequencing technologies. Butler, J. Genome Res. Many transcriptome reconstruction methods fall in the genome-guided category. They typically start by mapping sequencing reads onto the reference genome, using spliced alignment tools, such as TopHat [ 29 ] or SpliceMap [ 30 ].

The spliced alignments are used to identify putative exons, splice junctions and transcripts that explain the alignments. While some methods aim to achieve the highest sensitivity, others work to predict the smallest set of transcripts explaining the given input reads. Furthermore, some methods aim to reconstruct the set of transcripts that would insure the highest quantification accuracy. Scripture [ 1 ] construct a splice graph from the mapped reads and reconstructs transcripts corresponding to all possible paths in this graph.

It then uses paired-end information to filter out some transcripts. Although Scripture achieves very high sensitivity, it may predict a lot of incorrect isoforms. The method of Trapnell et al. TRIP [ 3 ] uses an integer programming model where the objective is to select the smallest set of putative transcripts that yields a good statistical fit between the fragment length distribution empirically determined during library preparation and fragment lengths implied by mapping read pairs to selected transcripts.

IsoLasso [ 32 ] uses the LASSO [ 33 ] algorithm, and it aims to achieve a balance between quantification accuracy and predicting the minimum number of transcripts. It formulates the problem as a quadratic program, with additional constraints to ensure that all exons and junctions supported by the reads are included in the predicted isoforms. CLIIQ [ 34 ] uses an integer linear programming solution that minimizes the number of predicted isoforms explaining the RNA-Seq reads while minimizing the difference between estimated and observed expression levels of exons and junctions within the predicted isoforms.

Traph [ 35 ] proposed a method based on network flows for a multiassembly problem arising from transcript identification and quantification with RNA-Seq. This probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multi-mapped reads. Alignment of RNA-Seq reads onto the reference genome, reference annotations, exon-exon junction libraries, or combinations thereof is the first step of RNA-Seq analyses, unless none of these are available in which case it is recommended to use de novo assembly methods [ 23 , 24 ].

The best mapping strategy depends on the purpose of RNA-Seq analysis. If the focus of the study is to estimate transcripts and gene expression levels rather then discover new transcripts then it is recommended to map reads directly onto the set of annotated transcripts using a fast tool for ungapped read alignment. To be able to discover new transcriptional variants one should map the reads onto the reference genome.

Recently, many bioinformatics tools, called spliced read aligners, have been developed to map RNA-Seq reads onto a reference genome [ 29 , 30 ]. Both spliced alignments and local alignments can be used to detect novel transcriptional and splicing events including exon boundaries, exon-exon junctions, gene boundaries, transcriptional start TSS and transcription end sites TES.

In our experiments we used TopHat [ 29 ] with default parameters. Typically, a gene can express multiple mRNA transcripts due to alternative transcriptional or splicing events including alternative first exon, alternative last exon, exon skipping, intron retention, alternative 5' splice site A5SS , and alternative 3' splice site A3SS [ 39 ].

To represent such alternative transcripts, a gene is processed as a set of so called 'pseudo-exons' based on alternative variants obtained from aligned RNA-Seq reads. A pseudo-exon is a region of a gene between consecutive transcriptional or splicing events, i. Hence, every gene consists of a set of non-overlapping pseudo-exons. This gene representation lets us easily enumerate all possible transcripts of a gene.

To generate the set of putative transcripts, we first create a splice graph based on pseudo-exon boundaries and splice junctions. An example of three transcripts, Tr 1 , Tr 2 and Tr 3. Each transcript is represented as a set of exons. Pseudo-exons are regions of a gene between consecutive transcriptional or splicing events. S psej and E psej represent the starting and ending position of pseudo-exon j , respectively. The splice graph is a directed acyclic graph Figure 3 whose vertices represent pseudo-exons and edges represent pairs of pseudo-exons immediately following one another in at least one transcript which is witnessed by at least one spliced read.

Both splice junctions and pseudo-exon boundaries are inferred from read alignments. Next, inferred splice junctions are used to partition the reference genome into a set of non-overlapping segments, which are classified as a intron, b pseudo-exon, or c combination of both. It is easy to classify a segment as pseudo-exon if it is entirely covered, and as intron in case it is entirely uncovered. Segments containing a combination of introns and exons most likely contain gene boundaries.

In this case we identify islands of coverage inside the segment. A segment may contain several coverage islands which correspond to single exon genes. Splice graph. The red horizontal lines represent single reads. Reads interrupted by dashed lines are spliced reads. Each vertex of the splice graph corresponds to a pseudo-exon and each directed edge corresponds to a splice junction between two pseudo-exons. Red vertices of the slice graph serve as transcription start sites TSS.

Blue vertices - transcription end sites TES. After constructing the splice graph, MaLTA enumerates all maximal paths using a depth-first-search algorithm. These paths correspond to putative transcripts. The next subsection presents a maximum likehood transcriptome assembly and quantification algorithm that selects a minimal subset of candidate transcripts that best fits the observed RNA-Seq reads.

The key ingredient is an expectation-maximization algorithm for estimating expression levels of candidate transcripts. Existing transcriptome assembly methods [ 3 , 4 ] use read pairing information and fragment length distribution to accurately assemble the set of transcripts expressed in a sample. This information is not available for current Ion Torrent technology, which can make it challenging to assemble transcripts.

Our approach is to simultaneously explore the transcriptome structure and perform transcriptome quantification using a maximum likelihood model. MaLTA starts from the set of putative transcripts and selects the subset of this transcripts with the highest support from the RNA-Seq data. Maximum likelihood estimates of putative transcripts are computed using an Expectation Maximization EM algorithm which takes into account alternative splicing and read mapping ambiguities.

EM algorithms are currently the state-of-the-art approach to transcriptome quantification from RNA-Seq read, and have been proven to outperform count-based approaches. Several independent implementations of EM algorithm exist in the literature [ 7 , 40 ]. IsoEM is an expectation-maximization algorithm for transcript frequency estimation.

It overcomes the problem of reads mapping to multiple transcripts using iterative framework which simultaneously estimates transcript frequencies and imputes the missing origin of the reads. A key feature of IsoEM, is that it exploits information provided by the distribution of insert sizes, which is tightly controlled during sequencing library preparation under current RNA-Seq protocols.

In [ 7 ], we showed that modeling insert sizes is highly beneficial for transcript expression level estimation even for RNA-Seq data consisting of single reads, as in the case of Ion Torrent. Modeling insert sizes contributes to increased estimation accuracy by disambiguating the transcript of origin for the reads.

In IsoEM, insert lengths are combined with base quality scores, and, if available, strand information to probabilistically allocate reads to transcripts during the expectation step of the algorithm. Since most Ion Torrent sequencing errors are insertions and deletions, we developed a version of IsoEM capable of handling insertions and deletions in read alignments. The main idea of the MaLTA approach is to cover all trancriptional and splicing variants presented in the sample with the minimum set of putative transcripts.

We use the new version of the IsoEM algorithm described above to estimate expression levels of putative transcripts. Since IsoEM is run with all possible candidate transcripts, the number of transcripts that are predicted to have non-zero frequency can still be very large. Instead of selecting all transcripts with non zero frequency, we would like to select a small set of transcripts that explain all observed splicing events and have highest support from the sequencing data.

To realize this idea we use a greedy algorithm which traverses the list of candidate transcripts sorted in descending order by expression level, and selects a candidate transcript only if it contains a transcriptional or splicing event not explained by the previously selected transcripts. Genes were defined as clusters of known transcripts defined by the GNFAtlas2 table. In our simulation experiments, we simulate reads together with spliced alignments to the genome; these alignments are provided to all compared methods.

We varied the length of single-end reads, which were randomly generated per gene by sampling fragments from known transcripts. All the methods were compared on datasets with various read length, i. Expression levels of transcripts inside each gene cluster followed uniform and geometric distributions. All reconstructed transcripts were matched against annotated transcripts. As in [ 4 ] and [ 32 ], two transcripts were assumed to match if and only if internal exon boundaries coordinates i.

We use sensitivity and positive predictive value PPV to evaluate the performance of different assembly methods. Sensitivity is defined as the proportion of assembled transcripts that match annotated transcripts, i. Positive predictive value PPV is defined as the proportion of annotated transcript sequences among assembled sequences, i. The most recent versions of Cufflinks version 2.

We explore the influence of read and fragment length on performance of assembly methods. Table 1 reports sensitivity and PPV of transcriptome assembly for reads of length bp, simulated assuming both uniform and geometric expression of transcripts. For all methods the difference in accuracy between datasets generated assuming uniform and geometric distribution is small, with the latter one typically having a slightly worse accuracy.

Thus, in the interest of space we present remaining results for datasets generated using uniform distribution. There is a strong correlation between the number of splicing events within the gene and the number of annotated transcripts. A high number of splicing events leads to increased number of candidate transcripts, which makes the selection process more difficult. To explore the behavior of the methods depending on number of transcripts per gene we divided all genes into categories according to the number of annotated transcripts and calculated the sensitivity and PPV within each such category.

Figures 4 a -4 b compare the performance of three methods Cufflinks, IsoLasso, MaLTA on simulated data with respect to the number of transcripts per gene. Table 2 compares assembly accuracy of Cufflinks, IsoLasso, and MaLTA for different combinations of read and fragment lengths: 50 bp, bp , bp, bp , bp, bp , bp, bp , bp, bp. Comparison with IsoLasso on the real datasets is omitted due to technical problems IsoLasso results were consistently incomparable to other methods.

Reads were mapped to the hg18 reference genome using TopHat2 with default parameters which is able to produce spliced alignment used by transcriptome assembly tools Table 3. Although UCSC annotations are known to be incomplete, we expect a significant proportion of assembled transcripts to be consistent with these annotations. Thus, the performance of transcriptome assembly methods was evaluated by the total number of assembled transcripts matching UCSC annotations.

Both methods assemble highest number of transcripts confirmed by reference annotations for GOG dataset. Cufflinks and MaLTA respectively were able to assemble 13, and 16, transcripts, of which 1, and 4, are known annotated transcripts. A large number of identified annotated transcripts were confirmed by both methods Figure 5. The GOG dataset contains the highest number of annotated transcripts confirmed by both methods; among identified annotated transcripts 1, transcripts were confirmed by both methods.

Consistency of transcriptome assembly. As described in [ 41 ], each TaqMan Assay was run in four replicates for each measured gene. Tables 5 and 6 show statistics for the size, number of mapped reads, and accuracy of gene expression levels estimated by IsoEM for each of the 10 datasets as well as the combined reads for each sample.

Figure 6 presents a comparison between IsoEM and Cufflinks results. The red color represents the 2nd quartile and the green color represents the 3rd quartile. Table 7 presents the results of this comparison, showing higher R 2 for IsoEM in both cases. Experimental results on both real and synthetic datasets generated with various sequencing parameters and distribution assumptions suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM compared to existing state-of-the-art approaches.

Nature Biotechnology. Lecture Notes in Computer Science.

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