Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes—compared with the human microbiota—especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Loss of Gene Information: Discrepancies between RNA Sequencing, cDNA Microarray, and qRT-PCR

Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, we compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, we found differences in the gene expression profiles of the aforementioned techniques. Here, we present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, we suggest that these findings need to be considered when dealing with data from transcriptome analyses.

Study of NTF-3 (Neurotrophic Factor 3) Gene Information in Columbidae by In Silico

Neurotrophin Factor 3 (NTF3) is one of the genes that plays an important role in the regulation of the neural systems of vertebrate animals, this gene has a special function in explaining the survival factors of some vertebrate animals. Based on the information obtained from GenBank, the nucleotide sequence of the NTF-3 gene in several vertebrate animals has been known and some of the data obtained have not been studied further for research purposes in adding information related to the molecular character of the NTF-3 gene, such as the NTF-3 gene in Columbidae. Columbidae is a group of birds that have quite diverse species variations, the number of species in columbidae will be very helpful in obtaining data on comparisons of the genetic character of the NTF3 gene. The purpose of this study was to analyze and describe the information on the NTF-3 gene (Neurotrophic Factor 3) in Columbidae through the in silico approach with computational methods. The NTF3 gene nucleotide sequences in Columbidae showed a fairly high level of similarity to the base sequences. This illustrates the fairly close proximity between each species. Geotrygon Montana is a species of Columbidae which has variations of the Base sequence which is quite different from other species. Evaluation of the model structure shows good stability of each target protein, all evaluation results describe a good structure, meaning that the conformation of each target sequence is in accordance with the sequence, so that the structure that is built has high accuracy with the actual model. The results of this research study can be a special description in explaining the genetic characteristics of several Columbidae species for the purposes of conservation measures or efforts to preserve Columbidae species at the molecular and population genetic levels.

The Full‑Length Transcriptome Sequencing and Identification of Na+/H+ Antiporter Genes in Halophyte Nitraria tangutorum Bobrov

Nitraria tangutorum Bobrov is a halophyte that is resistant to salt and alkali and is widely distributed in northwestern China. However, its genome has not been sequenced, thereby limiting studies on this particular species. For species without a reference genome, the full-length transcriptome is a convenient and rapid way to obtain reference gene information. To better study N. tangutorum, we used PacBio single-molecule real-time technology to perform full-length transcriptome analysis of this halophyte. In this study, a total of 21.83 Gb of data were obtained, and 198,300 transcripts, 51,875 SSRs (simple sequence repeats), 55,574 CDS (coding sequence), and 74,913 lncRNAs (long non-coding RNA) were identified. In addition, using this full-length transcriptome, we identified the key Na+/H+ antiporter (NHX) genes that maintain ion balance in plants and found that these are induced to express under salt stress. The results indicate that the full-length transcriptome of N. tangutorum can be used as a database and be utilized in elucidating the salt tolerance mechanism of N. tangutorum.

Uncertainties in synthetic DNA-based data storage

Deoxyribonucleic acid (DNA) has evolved to be a naturally selected, robust biomacromolecule for gene information storage, and biological evolution and various diseases can find their origin in uncertainties in DNA-related processes (e.g. replication and expression). Recently, synthetic DNA has emerged as a compelling molecular media for digital data storage, and it is superior to the conventional electronic memory devices in theoretical retention time, power consumption, storage density, and so forth. However, uncertainties in the in vitro DNA synthesis and sequencing, along with its conjugation chemistry and preservation conditions can lead to severe errors and data loss, which limit its practical application. To maintain data integrity, complicated error correction algorithms and substantial data redundancy are usually required, which can significantly limit the efficiency and scale-up of the technology. Herein, we summarize the general procedures of the state-of-the-art DNA-based digital data storage methods (e.g. write, read, and preservation), highlighting the uncertainties involved in each step as well as potential approaches to correct them. We also discuss challenges yet to overcome and research trends in the promising field of DNA-based data storage.

Cenote-Taker 2 Democratizes Virus Discovery And Sequence Annotation

Viruses, despite their great abundance and significance in biological systems, remain largely mysterious. Indeed, the vast majority of the perhaps hundreds of millions of viral species on the planet remain undiscovered. Additionally, many viruses deposited in central databases like GenBank and RefSeq are littered with genes annotated as “hypothetical protein” or the equivalent. Cenote-Taker 2, a virus discovery and annotation tool available on command line and with a graphical user interface with free high-performance computation access, utilizes highly sensitive models of hallmark virus genes to discover familiar or divergent viral sequences from user-input contigs. Additionally, Cenote-Taker 2 uses a flexible set of modules to automatically annotate the sequence features of contigs, providing more gene information than comparable tools. The outputs include readable and interactive genome maps, virome summary tables, and files that can be directly submitted to GenBank. We expect Cenote-Taker 2 to facilitate virus discovery, annotation, and expansion of the known virome.

KAUST Metagenomic Analysis Platform (KMAP), Enabling Access to Massive Analytics of Re-Annotated Metagenomic Data.

Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~27,000 public metagenomic samples captured in ~450 studies sampled across ~77 diverse habitats, resulting in 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP facilitates the exploration and comparison of microbial GITs across different habitats with over 275 million genes.