The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

Abstract

The study of microorganisms that pervade each and every part of this planet has encountered many challenges through time such as the discovery of unknown organisms and the understanding of how they interact with their environment. The aim of this review is to take the reader along the timeline and major milestones that led us to modern metagenomics. This new and thriving area is likely to be an important contributor to solve different problems. The transition from classical microbiology to modern metagenomics studies has required the development of new branches of knowledge and specialization. Here, we will review how the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution. New computational methods are constantly developed to collect, process, and extract useful biological information from a variety of samples and complex datasets, but metagenomics needs the integration of several of these computational methods. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design, which allows them to reveal the information in a metagenome.

Decoding Complex Soil Microbial Communities through New Age "Omics"

“…Some other problems associated with Metagenomics are cloning biases [35], sampling biases, misidentification of "decorating enzymes" and incorrect promoter sites in genomes and dispersion of genes involved in secondary metabolite production [36]. These biases could only be resolved using statistical approaches in order to detect the difference between the expected and observed bacterial diversity and to determine the actual species richness [37]. Cultivation based studies, once considered outdated is now been re-considered for microbial diversity studies.…”

Making big data smart—how to use metagenomics to understand soil quality

“…Rapid advances in sequencing technology, which each have their specific challenges, make it impossible to provide universal guidelines. With 454 pyrosequencing being outdated and long read technologies such as Oxford Nanopore Technologies and PacBio® yet not frequently used for metagenomics, here, we focus on Illumina-based technologies, which are currently the de facto standard in metagenomics (Sanchez-Flores et al 2015).…”

“…Since DNA extraction protocols vary in efficiency depending on the nature of the samples and in removing various inhibitors we recommend testing the workflow on a few non-essential samples first (Frostegård et al 1999). After a DNA extraction method has been selected, it should be used consistently given Gilbert et al 2014;Penton et al 2016;Prosser 2010;Salter et al 2014;Tatangelo et al 2014;Totsche et al 2010 Sequencing library preparation -Check for inhibitory effects -Avoid multiple displacement amplification (MDA) -Use your controls -Shear DNA for shotgun sequencing Salter et al 2014;Yilmaz et al 2010 Bioinformatic data analysis -Remove contaminants -Remove adapters -Quality & length filter -Estimate coverage -If possible, use mock community (defined mixture of microbial cells) to validate your workflow -Upload raw sequencing data to public server Bergkemper et al 2016;Darzi et al 2016;Del Fabbro et al 2013;Menzel et al 2016;Rodriguez-R and Konstantinidis 2014a;Sanchez-Flores et al 2015;Schmieder and Edwards 2011;Schubert et al 2016;Wood and Salzberg 2014 the inherent bias introduced throughout the whole project. Finally, depending on the aim of the study, one might also consider employing methods that separate extracellular DNA from intracellular DNA (Pietramellara et al 2009) which allow a discrimination between alive and dead microbes.…”

DNA Sequencing as a Tool to Monitor Marine Ecological Status

“…A growing number of assemblers (Boisvert et al, 2012;Peng et al, 2012) and binning tools (Alneberg et al, 2014;Imelfort et al, 2014;Nurk et al, 2017) are available that assemble short sequencing reads into larger contigs and subsequently combine them into bins based on, for example, sequence similarity (Teeling and Glöckner, 2012). In addition to standard biodiversity metrics such as alpha, beta, and gamma diversity (Escobar-Zepeda et al, 2015;Zhou et al, 2015), network analysis tools aid visualization of complex ecological relationships (Mitra et al, 2010;Hurwitz et al, 2014).…”

“…Work initiated in the area of standardizing laboratory procedures includes much-needed development of reference materials and improved DNA extraction methods, as exemplified by establishment of the International Metagenomics and Microbiome Standards Alliance (IMMSA) (NIST, 2016). Controls used to assess artifacts and bias introduced by sample preparation, PCR, and sequencing are reviewed elsewhere (Pinto and Raskin, 2012;Elbrecht and Leese, 2015;Pedersen et al, 2015;Tan et al, 2015;Zhou et al, 2015;Aylagas et al, 2016;Danovaro et al, 2016) and should be used in conjunction with standard quality control pipelines for sequence quality, including chimeric removal (Smyth et al, 2010;Teeling and Glöckner, 2012;Zhou et al, 2014;Escobar-Zepeda et al, 2015;Jeon et al, 2015). Including mock communities in DNA sequencing efforts can assess technical issues such as incomplete DNA extraction or library preparation, PCR, and sequencing errors (Schirmer et al, 2015) and provide correction factors (Tan et al, 2015;Aylagas et al, 2016).…”