MetaGT : A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data

While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available fromhttps://github.com/ablab/metaGT.Peer reviewe

Wochenende — modular and flexible alignment-based shotgun metagenome analysis

Background: Shotgun metagenome analysis provides a robust and verifiable method for comprehensive microbiome analysis of fungal, viral, archaeal and bacterial taxonomy, particularly with regard to visualization of read mapping location, normalization options, growth dynamics and functional gene repertoires. Current read classification tools use non-standard output formats, or do not fully show information on mapping location. As reference datasets are not perfect, portrayal of mapping information is critical for judging results effectively. Results: Our alignment-based pipeline, Wochenende, incorporates flexible quality control, trimming, mapping, various filters and normalization. Results are completely transparent and filters can be adjusted by the user. We observe stringent filtering of mismatches and use of mapping quality sharply reduces the number of false positives. Further modules allow genomic visualization and the calculation of growth rates, as well as integration and subsequent plotting of pipeline results as heatmaps or heat trees. Our novel normalization approach additionally allows calculation of absolute abundance profiles by comparison with reads assigned to the human host genome. Conclusion: Wochenende has the ability to find and filter alignments to all kingdoms of life using both short and long reads, and requires only good quality reference genomes. Wochenende automatically combines multiple available modules ranging from quality control and normalization to taxonomic visualization. Wochenende is available at https://github.com/MHH-RCUG/nf_wochenende

A standardized pipeline for isolation and assembly of genomes from symbiotic bacteria in whole louse genomic sequence data.

Many insects are known to harbour intracellular and heritable bacteria (endosymbionts), which provide their hosts with adaptive traits. Whole insect gDNA shotgun sequencing projects often sequence the genome of endosymbiont, in addition to the insect’s genome. There are approximately 600 whole genome shotgun libraries from insects available on the public repository (NCBI), which can be mined to obtain endosymbiont genomes. The assembly and annotation of endosymbiont genomes can contribute towards the exploration of their role as obligate symbiotic partners. However, de novo assembly of an endosymbiont genome, continues to be challenging, when the host and/or enteric bacterial gDNA is present in the library as well. So far, whole genome sequence data has been mined by investigators, who manually interrogate the data at multiple steps, a process that is time consuming and difficult to replicate. Here I developed and evaluated a novel strategy that reduces intervention required by the researcher. The strategy consists of two steps: 1) filtering of de novo assembled endosymbiont contigs using Blastn search against a custom reference database and 2) reconstruction of the genome through de novo assembly of reads associated with filtered contigs. Illumina HiSeq libraries were simulated in silico and the pipeline was deployed using the simulated data to test the efficacy of the method. The mean endosymbiont genome recovery from simulated data was 91.27% with a range of 100%-76%. When the method was tested with “real” whole louse shotgun sequencing libraries obtained from a public repository, the results were mixed. The strategy was accurate in contig selection when the louse contained an endosymbiont which had a small genome enriched for AT bases, with a mean percent genome recovery of 98.38% and the range of 100% - 95.88%. However, in other cases involving symbionts with larger genomes, the resulting genomes appeared to be incomplete and require further evaluation

A Benchmark of Genetic Variant Calling Pipelines Using Metagenomic Short-Read Sequencing

Microbes live in complex communities that are of major importance for environmental ecology, public health, and animal physiology and pathology. Short-read metagenomic shotgun sequencing is currently the state-of-the-art technique for exploring these communities. With the aid of metagenomics, our understanding of the microbiome is moving from composition toward functionality, even down to the genetic variant level. While the exploration of single-nucleotide variation in a genome is a standard procedure in genomics, and many sophisticated tools exist to perform this task, identification of genetic variation in metagenomes remains challenging. Major factors that hamper the widespread application of variant-calling analysis include low-depth sequencing of individual genomes (which is especially significant for the microorganisms present in low abundance), the existence of large genomic variation even within the same species, the absence of comprehensive reference genomes, and the noise introduced by next-generation sequencing errors. Some bioinformatics tools, such as metaSNV or InStrain, have been created to identify genetic variants in metagenomes, but the performance of these tools has not been systematically assessed or compared with the variant callers commonly used on single or pooled genomes. In this study, we benchmark seven bioinformatic tools for genetic variant calling in metagenomics data and assess their performance. To do so, we simulated metagenomic reads to mimic human microbial composition, sequencing errors, and genetic variability. We also simulated different conditions, including low and high depth of coverage and unique or multiple strains per species. Our analysis of the simulated data shows that probabilistic method-based tools such as HaplotypeCaller and Mutect2 from the GATK toolset show the best performance. By applying these tools to longitudinal gut microbiome data from the Human Microbiome Project, we show that the genetic similarity between longitudinal samples from the same individuals is significantly greater than the similarity between samples from different individuals. Our benchmark shows that probabilistic tools can be used to call metagenomes, and we recommend the use of GATK's tools as reliable variant callers for metagenomic samples

Exploring environmental intra-species diversity through non-redundant pangenome assemblies

At the genome level, microorganisms are highly adaptable both in terms of allele and gene composition. Such heritable traits emerge in response to different environmental niches and can have a profound influence on microbial community dynamics. As a consequence, any individual genome or population will contain merely a fraction of the total genetic diversity of any operationally defined "species", whose ecological potential can thus be only fully understood by studying all of their genomes and the genes therein. This concept, known as the pangenome, is valuable for studying microbial ecology and evolution, as it partitions genomes into core (present in all the genomes from a species, and responsible for housekeeping and species-level niche adaptation among others) and accessory regions (present only in some, and responsible for intra-species differentiation). Here we present SuperPang, an algorithm producing pangenome assemblies from a set of input genomes of varying quality, including metagenome-assembled genomes (MAGs). SuperPang runs in linear time and its results are complete, non-redundant, preserve gene ordering and contain both coding and non-coding regions. Our approach provides a modular view of the pangenome, identifying operons and genomic islands, and allowing to track their prevalence in different populations. We illustrate this by analysing intra-species diversity in Polynucleobacter, a bacterial genus ubiquitous in freshwater ecosystems, characterized by their streamlined genomes and their ecological versatility. We show how SuperPang facilitates the simultaneous analysis of allelic and gene content variation under different environmental pressures, allowing us to study the drivers of microbial diversification at unprecedented resolution

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers

A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or “classifier”. Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce “META Score”: a unified, quantitative value which rates an analytic classifier’s ability to both identify and count taxa in a representative sample

Metagenomics: A viable tool for reconstructing herbivore diet

Metagenomics can generate data on the diet of herbivores, without the need for primer selection and PCR enrichment steps as is necessary in metabarcoding. Metagenomic approaches to diet analysis have remained relatively unexplored, requiring validation of bioinformatic steps. Currently, no metagenomic herbivore diet studies have utilized both chloroplast and nuclear markers as reference sequences for plant identification, which would increase the number of reads that could be taxonomically informative. Here, we explore how in silico simulation of metagenomic data sets resembling sequences obtained from faecal samples can be used to validate taxonomic assignment. Using a known list of sequences to create simulated data sets, we derived reliable identification parameters for taxonomic assignments of sequences. We applied these parameters to characterize the diet of western capercaillies (Tetrao urogallus) located in Norway, and compared the results with metabarcoding trnL P6 loop data generated from the same samples. Both methods performed similarly in the number of plant taxa identified (metagenomics 42 taxa, metabarcoding 43 taxa), with no significant difference in species resolution (metagenomics 24%, metabarcoding 23%). We further observed that while metagenomics was strongly affected by the age of faecal samples, with fresh samples outperforming old samples, metabarcoding was not affected by sample age. On the other hand, metagenomics allowed us to simultaneously obtain the mitochondrial genome of the western capercaillies, thereby providing additional ecological information. Our study demonstrates the potential of utilizing metagenomics for diet reconstruction but also highlights key considerations as compared to metabarcoding for future utilization of this technique

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream

정확한 서열정렬기법과 인메모리 핵심 유전자 데이터베이스 기반의 향상된 메타유전체 분류법

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2020. 8. 천종식.샷건 메타지노믹스는 미생물과 숙주 또는 환경사이의 미치는 영향을 이해하는데 매우 중요한 역할을 하고 있다. 기술의 발달과 더불어 메타지노믹스를 통한 올바른 미생물 종의 동정과 각 종들의 분포는 마이크로바이옴 연구의 핵심 구성요소가 되었으며, 지난 10년간 샷건 메타지노믹스 분석을 위한 여러 알고리즘과 데이터베이스들이 개발되어져 왔다. 하지만 서로 다른 기준 데이터 혹은 알고리즘을 사용한 방법들은 서로 다른 분류 정보와 분석 파이프라인으로 인하여 편향된 결과를 나타내기도 하였는데, 이를 보완하고 보다 정확한 분류 동정을 위해 배양이 어려운 표준 균주와 같은 다양한 균주의 유전체 데이터를 포함하는 기준 데이터베이스의 중요성이 대두되고 있다. 샷건 메타지노믹스 분석에서 또 다른 중요한 요소는 분석에 소요되는 시간이라 할 수 있는데 대부분의 생물정보학적 프로그램들은 계산을 수행함에 있어 메모리와 알고리즘 최적화가 되어있지 않아 분석에 상당한 시간이 소요되는 문제점이 있다. 이러한 문제를 해결하기 위해, 본 연구에서는 exact match k-mer classification과 같은 방법을 사용하여 분석 속도를 향상시켰으며 Up-to-date Bacterial Core Gene (UBCG)를 기준 데이터베이스로 사용하여 보다 정확한 샷건 메타지노믹 분석을 수행할 수 있게 하였다. 분석의 효율성을 높이기 위해 두개의 기준 UBCG 데이터베이스가 만들어 졌으며 한 개는 박테리아의 분류체계에서 유효한 종명 (Valid names)만을 가지고 있는 데이터베이스와 다른 하나는 유효한 종명과 함께 EzBioCloud에 있는 genomospecies를 가지고 생성하였다. 검증을 위해 Streptococcus 종을 포함하는 (i) 합성된 메타지놈 샘플과 (ii) 만성 폐쇄성 폐질환(COPD) 환자의 임상 검체 (iii) 혈류 감염 환자의 임상 검체로 이루어진 세개의 데이터 셋을 이용하였으며 기존에 널리 알려진 샷것 파이프라인인 MetaPhlan2과 본 연구의 파이프라인을 비교 분석하였다. 위 검증 분석에서 UBCG를 기준 서열로 사용하기에 충분함을 검증하였으며, 빠르고 정확하게 기준 유전체에서 UBCG 서열을 뽑아 샷건 분석에 용이함을 증명하였다. 또한 genomospecies를 기준 데이터베이스에 추가함으로써, 보다 개선된 분류 정확도를 얻을 수 있음을 제시하였다. 마지막으로 비록 여러 파이프라인과 데이터베이스들이 존재하지만 보다 신뢰할 수 있는 분류결과를 얻기 위해선 기준 데이터베이스의 지속적인 업데이트와 분류 체계의 검증의 중요함을 강조하였다. 이후 본 연구에서 개발된 파이프라인을 이용하여 4,000개의 샷건 메타지놈 샘플에서 사람에 장내에 가장 많이 발견되는 Bacteroides 종에 대한 분석을 수행하였다. 많은 양의 데이터를 분석하여야 하기 때문에 기존에 많이 사용되는 MetaPhlAn2 과 같은 방법은 사용할 수 없었으며 분석 결과 Bacteroides는 도시화된 사람에게 많이 분포하는 반면 아프리카 혹은 남미지역에서 원시적 부족의 삶을 사는 사람에게서는 상대적으로 적게 분포함을 확인할 수 있었다. 또한 각 나라별 인구에서는 우점되는 Bacteroides 종이 다름을 확인할 수 있었는데 이는 각 연구의 샘플링 방법 혹은 위치에 따라 설명되어 질 수 있었다. 실험용 쥐의 결과에서는 가장 다양한 Bacteroides를 관찰할 수 있었으며 이는 많은 수의 기준 유전체가 생쥐에게서 나왔기 때문인 것으로 생각된다. 또한 고양이나 강아지 같은 반려동물의 샘플에서도 높은 상관관계를 발견할 수 있었는데 각 동물들의 생활양식과 먹이에 따른 결과인 것으로 보인다. 본 연구를 통해 보다 많은 메타지놈 데이터 분석의 필요성을 강조하고 있으며, 핵심 유전자들을 기준 데이터로 사용하는 방법의 실효성과 성능을 검증하였다. 이러한 핵심 유전자 기반의 기준 데이터베이스는 보다 정확하고 전체 미생물의 풍부도를 예측하는데 중요한 역할을 하는 것을 확인하였고 k-mer 방법을 통해 기존에 존재하던 다른 파이프라인 보다 더욱 빠른 결과를 도출할 수 있었다. 마지막으로 빠르게 기준 데이터베이스를 만들 수 있기 때문에 항상 최신의 데이터를 가지고 분석을 수행할 수 있으며 이는 궁극적으로 본 연구의 파이프라인을 실질적으로 연구나 진단 목적으로 이용하는 연구자들에게 큰 도움이 될 것이다.Shotgun metagenomics is of great importance to understand the microbial community composition of a sample and the impact it has on its host. The proper identification and quantification of bacterial species is a key component of any microbiome research that is based on metagenomic samples. In the last decade, several algorithms and databases have been developed, however the differences between references and the type of algorithm used for the classification makes the comparisons among themselves unfair and bias. The contents of the reference database, including genome sequences of type strains or reference genomes of uncultured species, have a great impact on the performance of the classification results of metagenomic samples. Another significant factor on shotgun metagenomics is the classification speed as most current bioinformatic tools lack computational and memory optimization. Here, I propose several enhancements to a well-known method, exact match k-mer classification in order to increase the overall speed of a metagenomic classification. This method was further improved by the use of Up-to-date Bacterial Core Gene (UBCG) sequences to provide better method for a faster and accurate shotgun metagenomic profiling classification. In order to prove the efficiency of our method, I built two UBCG-based reference databases: one containing UBCG sequences of valid named species, and the second one containing UBCG sequences of all valid named species and genomospecies in the EzBioCloud database. Three datasets containing Streptococcus species were used to evaluate the improved method against the MetaPhlan2 tool which is the most widely used open-source shotgun metagenomic classifier: (i) synthetic metagenomic samples, (ii) clinical sputum samples from patients with chronic obstructive pulmonary disease (COPD), and (iii) clinical samples of a blood stream infection. In this analysis, I demonstrated that UBCG sequences can be used as references for metagenomic classification, showing that they are easy to extract from genome sequences and accurate when predicting relative abundance. I also showed that the inclusion of genomospecies in the reference databases, significantly improves the classification accuracy of bacterial species within a metagenomic sample. Finally, I showed that while publicly available pipelines and databases are easily accessible, for accurate and reliable taxonomic classification, an updated database with proper taxonomic and genomic curation must be used. The method devised in this work is then applied to profile the Bacteroides species in over 4,000 shotgun metagenomic samples, which is one of most abundant members of the human gut microbiome. This task cannot be accomplished using conventional tools such as MetaPhlAn2 due to the high processing time they require. The results in this study showed that Bacteroides is high abundant in human samples from urban areas while being low abundant in humans from rural areas, particularly African and South American tribes. Countries showed dominance for a specific Bacteroides species, but this could also be explained by the type of study were the samples came from. Mice samples showed the most diversity of Bacteroides, this can be attributed by the number of bacterial references isolated from this organism. House cat and dog samples showed correlation between each other, this may be attributed to the similarities of their lifestyle and diet. This study shows the importance of having a great number of samples for any given metagenomic analysis, and even though, we have profiled thousands of samples, more might be needed in the future. The method proposed in this thesis demonstrates that core genes are reliable reference sequences for shotgun metagenomics. Their implementation as reference sequences in metagenomic databases improves the accuracy of the abundance prediction of any given sample. Additionally, with the use of a k-mer approach, this methods running time outperforms the most popular shotgun metagenomic tools. The work presented in this thesis aims to help microbial research by providing faster and accurate metagenomic taxonomic predictions. Finally, with the ability of updating a metagenomic database with ease, will help researchers to obtain the most up-to-date results to find potential diagnosis or treatments for diseases associated to human microbial communities.Chapter 1. General Introduction 1 1.1. Introduction to metagenomics 2 1.2. 16S rRNA sequencing 3 1.3. Shotgun metagenomic sequencing 5 1.3.1. History 5 1.3.2. Sample extraction 7 1.3.3. Library preparation 8 1.3.4. Sequencing 8 1.4. Shotgun metagenomic classification 9 1.4.1. Homology-based approaches 9 1.4.2. Exact match K-mer approaches 11 Chapter 2. An exact match k-mer algorithm 13 2.1. An exact match k-mer classification approach 14 2.1.1. Definition of the problem 14 2.1.2. Building a k-mer reference database 14 2.1.2.1. K-mer counting 14 2.1.2.2. K-mer mapping 16 2.1.3. Classification of a metagenomic read 16 2.1.3.1. K-mer search 19 2.1.3.2. Scoring a metagenomic read 20 2.1.4. Calculating the metagenome profile 20 2.1.4.1. Normalization for LCA-assigned reads 21 2.1.4.2. Normalization for cell count relative abundance 22 2.2. RAM memory usage 22 2.3. Quality Control 23 2.3.1. Read Trimming 23 2.3.2. Host read removal 24 Chapter 3. Revealing unrecognized species in the genus Streptococcus 28 3.1. A brief history of streptococcus in clinical metagenomics 29 3.2. Results and Discussion 32 3.2.1. Building a core gene reference database 32 3.2.2. Evaluation of Pipelines using Synthetic Metagenomes 36 3.2.3. Chronic obstructive pulmonary disease samples 44 3.2.3. Evaluating the value of genomospecies references in a metagenomic database 56 3.2.4. Identifying accurately a Streptococcal infection using clinical data 63 3.2.5. Effects of different ANI thresholds on the classification of genomospecies 69 3.3. Materials and Methods 76 3.3.1. Selecting the reference genomes 76 3.3.2. Average nucleotide identity and hierarchical clustering 76 3.3.3. Synthetic and Real metagenomic samples 77 3.3.4. Extracting the core genes 77 3.3.5. Taxonomic profiling 83 3.3.6. Biomarker discovery 84 3.4. Conclusions 85 Chapter 4. A large-scale shotgun metagenomic analysis on Bacteroides 86 4.1. Introduction 87 4.2. Bacteroides on the human gut 89 4.2.1. Collecting the samples 89 4.2.2. Methods 89 4.2.2.1. Reference Genomes 89 4.2.2.2. Metagenome profiling 90 4.2.3. Results 103 4.3. Bacteroides on Animal Species 128 4.3.1. Methods 128 4.3.2. Results 128 4.4. Discussion and conclusions 133 General Conclusion 135 References 139 Appendix I. A list of genomes from the genus Streptococcus used on Chapters 3 analysis. 146 국문초록 155Docto