Next-generation sequencing: applications beyond genomes.

The development of DNA sequencing more than 30 years ago has profoundly impacted biological research. In the last couple of years, remarkable technological innovations have emerged that allow the direct and cost-effective sequencing of complex samples at unprecedented scale and speed. These next-generation technologies make it feasible to sequence not only static genomes, but also entire transcriptomes expressed under different conditions. These and other powerful applications of next-generation sequencing are rapidly revolutionizing the way genomic studies are carried out. Below, we provide a snapshot of these exciting new approaches to understanding the properties and functions of genomes. Given that sequencing-based assays may increasingly supersede microarray-based assays, we also compare and contrast data obtained from these distinct approaches

Next generation sequencing

Informacije pohranjene u DNA sekvenci sadrži vrijedne podatke o tom organizmu, i znajući sekvencu cijelog genoma, u mogućnosti smo razumjeti procese koji se odvijaju u tom biću. Tijekom proteklih nekoliko godina, masivno paralelne metode DNA sekvenciranja su postale široko dostupne te se cijena sekvenciranja značajno snizila. Značajno brži i jeftiniji načini sekvenciranja značajno mijenjaju ne samo brojne projekte uključene u sekvenciranje genoma, već mijenjaju tok bioloških znanosti i aplikacije informacija dobivenih sekvenciranjem. Prve razmijene metode masivno paralelnog sekvenciranja su metode koje su se bazirale na klonalnoj amplifikaciji fragmenata DNA sekvence. Umnažanjem fragmenata, sekvenceri primaju mnogo intenzivniji signal o ugrađenoj bazi, no sam proces amplifikacije podložan je brojnim greškama zbog čega je i finalna sekvenca manje precizno određena. Sljedeća, treća, generacija sekvencera koristi se samo jednom molekulom fragmenta i sekvenciranje se provodi u realnom vremenu. Ove platforme su često preciznije od metoda prijašnje generacije i mogu se koristiti za kvantitativna istraživanja. Najnovije i najperspektivnije platforme su one temeljene na sekvenciranju pomoću nanopora. Zbog malih dimenzija prostora u kojem se odvija određivanje slijeda baza, velike količine DNA molekula bi se mogle sekvencirati u jednoj reakciji, na jednom stroju. Ove metode su još uvijek u razvoju zbog brojnih problema s kojima su se susreli kreatori ovih sekvencera pri radu u ovako malim dimenzijama. Ovaj rad detaljnije opisuje svaku od metoda masivno paralelnog sekvenciranja, navodeći prednosti i nedostatke svake od platformi kao i njihovu moguću primjenu.Information stored in DNA sequence contains valuable data about that organism, and by knowing ones genome, we will be able to understand processes happening in that being. Over the past few years, massively parallel DNA sequencing platforms have become widely available dramatically reducing the cost of DNA sequencing. The fast and low-cost sequencing approaches not only change the landscape of genome sequencing projects but also usher in new opportunities for sequencing in various applications. First developed massively-parallel sequencers were those that used clonally amplified fragments of DNA. By amplifying the fragments, sequencers would receive much stronger signal of incorporated nucleotide, but amplification leads to many errors causing less accurate final sequence. Later developed, third generation of sequencers uses single molecule templates and sequencing is usually done in real time. These methods are more accurate but also they can be used in quantitative applications. The newest and the most promising platforms are ones based on nanopore sequencing. Because of small dimensions of sequencing space, enormous amount of DNA could be sequenced in just one reaction, on one sequencer. These methods are still under development because of lot of problems designers of these machines encountered when doing in such small space. This thesis introduces into the high-throughput sequencing technologies, advantages and disadvantages of every platform and their biological application

Virtual Environment for Next Generation Sequencing Analysis

Next Generation Sequencing technology, on the one hand, allows a more accurate analysis, and, on the other hand, increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA- Seq, generates millions of short sequence fragments in a single run. These fragments, or reads, can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution is a distributed architecture consisting of a Grid Environment and a Virtual Grid Environment, in order to reduce processing time by making the system scalable and flexibl

Next-generation sequencing

Next-generation sequencing (also known as massively parallel sequencing) technologies are revolutionising our ability to characterise cancers at the genomic, transcriptomic and epigenetic levels. Cataloguing all mutations, copy number aberrations and somatic rearrangements in an entire cancer genome at base pair resolution can now be performed in a matter of weeks. Furthermore, massively parallel sequencing can be used as a means for unbiased transcriptomic analysis of mRNAs, small RNAs and noncoding RNAs, genome-wide methylation assays and high-throughput chromatin immunoprecipitation assays. Here, I discuss the potential impact of this technology on breast cancer research and the challenges that come with this technological breakthrough

Single cell transcriptome analysis using next generation sequencing.

The heterogeneity of tissues, especially in cancer research, is a central issue in transcriptome analysis. In recent years, research has primarily focused on the development of methods for single cell analysis. Single cell analysis aims at gaining (novel) insights into biological processes of healthy and diseased cells. Some of the challenges in transcriptome analysis concern low abundance of sample starting material, necessary sample amplification steps and subsequent analysis. In this study, two fundamentally different approaches to amplification were compared using next-generation sequencing analysis: I. exponential amplification using polymerase-chain-reaction (PCR) and II. linear amplification. For both approaches, protocols for single cell extraction, cell lysis, cDNA synthesis, cDNA amplification and preparation of next-generation sequencing libraries were developed. We could successfully show that transcriptome analysis of low numbers of cells is feasible with both exponential and linear amplification. Using exponential amplification, the highest amplification rates up to 106 were possible. The reproducibility of results is a strength of the linear amplification method. The analysis of next generation sequencing data in single cell samples showed detectable expression in at least 16.000 genes. The variance between samples results in a need to work with a greater amount of biological replicates. In summary it can be said that single cell transcriptome analysis with next generation sequencing is possible but improvements leading to a higher yield of transcriptome reads is required. In the near future by comparing single cancer cells with healthy ones for example, a basis for improved prognosis and diagnosis can be realised

A normalization technique for next generation sequencing experiments

Next generation sequencing (NGS) are these days one of the key technologies in biology. NGS' cost effectiveness and capability of finding the smallest variations in the genome makes them increasingly popular. For studies aiming at genome assembly, differences in read count statistics do not affect the outcome. However, these differences bias the outcome if the goal is to identify structural DNA characteristics like copy number variations (CNVs). Thus a normalization step must removed such random read count variations subsequently read counts from different experiments are comparable. Especially after normalization the commonly used assumption of Poisson read count distribution in windows on the chromosomes is more justified. Strong deviations of read counts from the estimated mean Poisson distribution indicate CNVs