April 2018-Long Read Sequencing

April

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April 2018

Introduction

Welcome back again RSG friends! The April newsletter of the Regional Student Group of Germany of the iSCB Student Council is out, and for this month we are going to talk about long sequencing reads technology, possible applications, and limitations of this technique. We will also talk about the computational techniques that have been developed to align such long reads during the last years. The newsletter is split into three parts: two parts are related to what kind of improvement we expect from long reads technology to address molecular discoveries and possible therapeutic application. The third part is revisiting the current state of the art in the computational techniques that are used to align long reads datasets. Have fun and see you again in May! Yours:

RGS Germany (ISCB Student Council) – Tommaso Andreani, Yvonne Gladbach, Neetika Nath, Nikos Papadopoulos

 

Highly parallel direct RNA sequencing on an array of nanopores

The transcriptome contains the expression of genetic information that characterizes the phenotype of an organism. The development of high-throughput next-generation sequencing (NGS) has powered studying transcriptomes, includes different expression levels of transcripts, and variations in gene structures (such as splicing or fusion genes), through sequencing of complementary DNA (cDNA). In order to perform downstream bioinformatics analysis, the data retrieved from the cells should be of high coverage, strand-specific, and capable of detecting the presence of modified bases that can reveal variations in gene structures. These cDNA strands are amplified by polymerase chain reaction (PCR), which can introduce bias such as loss of modifications in the RNA, distortion of relative cDNA and dropout of some RNA species.

To reduce such biases, Oxford Nanopore Technologies (ONT) has developed the nanopore-based platform, the MinION device. It is designed to detect RNA molecules without any need of enzymatic synthesis reaction, hence why it is called “direct RNA sequencing”. As the RNA molecule passes through an ion channel (“nanopore”), the different RNA bases create a different change in current. These changes are then translated to base sequences with an inbuilt method implementing a recurrent neural network (RNN). This suggests the measurement of RNA directly so that from ONT data it is possible to detect nucleotide analogues in a strand-specific manner. The machine can produce reads with a maximum length up to a few hundred thousand base pairs, which are particularly useful in the study of splicing events detection.

Nonetheless, there are several areas where an improvement is needed for direct RNA sequencing methods. Most software developed for sequencing applications, including RNN,  is not optimized to analyze direct RNA sequencing data, which leads to lower coverage of the data. Also, the detection of splicing variants can be misleading if degraded RNA is present in the sample. For the USB-sized MinION, clinical applications are not far ahead but an improvement in throughput (compared with Illumina sequencing)  will be required, for example when targeting regions likely to contain a gene variations.

Garalde, Daniel R., et al. “Highly parallel direct RNA sequencing on an array of nanopores.” Nature methods (2018).

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Innovation and challenges in detecting long read overlap: an evaluation of the state-of-art

One of the challenges in sequencing technology is the capability to sequence nucleic acid bases with high-throughput performance and high resolution, where, “resolution” encompasses the number of reads sequenced and the length of the respective sequences. Shotgun sequencing and next generation sequencing attempt to solve the problem by increasing the number of read fragments that are sequenced. However, there is also the option of trying to increase the read length. In order to address this problem, two companies have decided to implement a sequencing chemistry that allows to sequence long reads from 15000 up to 100000 base pairs. These two companies, named Pac Bio (PB) and Oxford Nanopore Technology (ONT), are the pioneer in this field and computational challenges started to emerge in order to handle long reads datasets. However, long read sequencing chemistry comes with its own set of challenges.

PB sequencing suffers from the introduction of insertions, deletions, and mismatches in the alignment, caused by base calling errors in the order of 16%. Apart from the BLASAR aligner, most common tools do not consider in their quality tests the error specific quality that is given by the introduction of structural and single point variants. However, this limitation can be at least partially addressed with the usage of a hairpin adaptor to both sides of a linear DNA sequence.

ONT uses a cloud-based service called Metrichor that implements a hidden Markov model (HMM) with states of every possible 6-mer. In the current HMM base calling methodology, if one state is identical to its next state, no net change in the sequence can be detected. This means that homopolymer states longer than six cannot be captured as they would be collapsed into a single 6-mer. These biases create an overall error rate in the base calling of 30-40%, that can be reduced to 10-20% thanks to the generation of two-direction reads. As in PB, this involved the usage of a hairpin adaptor allowing the nanopore to process both the forward and reverse strand of a sequence.

There are different aligners for long reads sequencing datasets and they differ in respect of the alignment procedure: alignment tracepoints and overlap regions. Alignment tracepoints approach is implemented in DALIGNER. Briefly, the reads are aligned using a local alignment procedure that will output a traces point map used to compute the full alignment of the reads. The main limitation of this algorithm is speed, and users need to split the dataset into small pieces in order to increase the efficiency during the alignment. Other tools like BLASR and GraphMap are typically used for PB datasets, while MHAP was specifically designed for ONT datasets. Minimap is another aligner that combines concepts of DALIGNER, MHAP, and GraphMap.

Chu, Justin, et al. “Innovations and challenges in detecting long read overlap: an evaluation of the state-of-the-art.” Bioinformatics 33.8 (2016): 1261-1270.

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A single-molecule long-read survey of the human transcriptome

Recent advances in short read RNA-sequencing afford snapshots of the transcriptome, revealing which genes are transcribed and to what amount. These snapshots reveal little additional information about longer RNA molecules though.  More answers may be provided by what many consider the 3rd generation of sequencing technologies, enabling the sequencing of long single molecules without amplification. The moment has come when an entire RNA molecule can be sequenced from the 5’ to the 3’ end thanks to protocols from Pacific Biosciences (PacBio) or Oxford Nanopores (ONT). While there are some issues complicating the sequencing of longer RNA molecules, nevertheless, complete intron structures are often preserved. With this technology, more than 10% of previously unknown RNA structures could be annotated.

With single-molecule long reads, the previously elusive full-length transcript isoforms can be tackled and more insights in the expression of long non-coding RNA and its isoforms can be gained. The sequencing platform of PacBio shows no context-specific errors and produces long reads up to ~7kbp. Compared to the 2nd generation sequencing, the error rate in these reads is still very high, so that improving the read lengths and the base-calling algorithms for the PacBio platform are required.

One such improving approach is based on deriving high-quality, single-molecule, circular-consensus (CCS) reads. Its limiting factor is the cDNA-template size which is often <1.5kbp and therefore smaller than the complete read length of the PacBio platform. All introns of the original transcript are represented in the CCS reads including the 5’ exons. Little sequence loss can be observed by 1.5kbp reads. The longer the transcript is, the longer the sequence of missing nucleotides. Evaluating the completeness of the full-length RNA from 5’ to 3’ ends was done with a Hidden Markov Model to identify whether the molecules start or end with a poly-A tail. 67% of CCS reads corresponded to polyadenylated RNAs of high quality, but some CCS reads spanned entire cDNA with lower quality. Using GMAP aligner introns, pre-mRNAs and exons could be mapped to hg19. The unannotated transcripts belonged to protein-coding genes, spliced gene-class including the long noncoding RNA genes, or pseudogenes. These classes also represent a challenge due to lower expression levels and therefore higher propensity for noise.

Overall, the represented approach is depending on the completeness of the cDNA synthesis, but not on amplification or fragmentation like other approaches. It addresses the complete understanding of all spliced RNAs within a transcriptome with improved read length and base-calling algorithms instead of error correction or hybrid long reads based on high-quality short reads.

Sharon, Donald et al. “A single-molecule long-read survey of the human transcriptome.” Nature Biotechnology 33.11 (2013): 1009-1014.