To train our model more effectively, we use NGS 16S to analyze the microbiota in the soil. We spray biostimulators into the soil to affect the entire microbiota and use NGS 16S regularly. By analyzing multiple NGS data, we can determine the nature of the relationships about bacteria and take advantage of these characteristics to add biostimulators again. Through this process training our model, we adjust to our system again and again, allowing our system predicts the changes of microbiota in soil accurately.
Team:NCTU Formosa/Dry Lab/NGS Data Analysis
What is NGS 16S?
Next, we will interpret NGS and 16S rRNA separately.
NGS
NGS(Next Generation Sequencing) is a kind of technique to sequence a number of genomes in very short time. There are three main platforms on the market currently: Solexa from Illumina, SOLiD from ABI, and 454 from Roche. The procedures of these three sequencers are different, but they are all based on chain termination.
Take Solixa for example, following these steps below:
1. Use ultrasound to break original DNA sequences into fragments of about 200-500 base pairs, and then attach the adapters to both ends of the fragments.
2. Place the DNA fragments on a flowcell with complementary adapter sequences on the surface. The adapters will adhere to each other to allow the DNA fragments stay at the flow cell.
3. Amplify DNA fragments by bridge amplification.
4. The sequencing uses the method like the Sanger sequencing, adding different bases (dNTPs) and synthetic reagents that have been calibrated for specific removable fluorescent molecules. Repeatedly the process of removing and detecting fluorescence. Last, the computer software will analysis large numbers of DNA sequences quickly.
FASTQC
FASTQC is mainly used to filter the NGS data. It is very important to check the data quality before analyzing the data. If the data quality is high , you can continue the next step.
After we input NGS sequence data into FASTQC, the program will analyze automatically and score each sequence to ensure that the quality of the gene sequences is suitable for computer calculation.
16S rRNA
16S rRNA is an important component of the ribosomal small subunit of prokaryote. The sequence contains several conserved regions and 9 hypervariable regions (V1 to V9). The hypervariable regions have genus or species specificity, considered to be the most suitable indicator for phylogeny of bacteria and identification of classification. NGS 16S uses the sequence of V4 and V5 in the hypervariable regions to detect the bacterial clusters.
Sampling
We divide the agricultural land into four large blocks of A, B, C, and D. In each block, there are three strips of 1, 2, and 3, each of which is divided into T, M, and D. Thus, we have thirty six sample in total( A1T, A2M, ....). We get 50 micro liters per sample from 10 to 15 centimeters depth near the root of each testing plant. Then, the samples are sent to the company for NGS analysis.
The result will present the each bacteria ratio in each samples and report it in an OTU table.(Fig. 1)
Marker Gene Amplicon Analysis
Microbiome data are generated from 16S ribosomal RNA(rRNA) gene. The PCR primers were designed to amplify the V4 region of the bacterial 16S ribosomal DNA. After profiling 16S rRNA sequencing, we used QIIME to generate operational taxonomic units (OTUs) table. Then we used bioinformatics tools and statistics methods to analyze microbial diversity in soil samples. We also used machine learning to predict how soil microbiota changes with addition of bio-stimulators.
Operational Taxonomic Units Table (OTUs Table)
Figure 1 is an example of OTUs table. Each column represents the type and amount of bacteria (OTU1, OTU2, …, OTU7) in each soil sample (A1, A2, A3, B1, B2, and B3). We generate seven tables for each level: Phylum, Class, Order, Family, Genus, and Species.
Figure 1: Schematic Diagram of Operation Taxnomy Unit table
Data Analysis Process
Figure 2: The process of correlation analysis
The OTUs tables will consist of unclassified names using the open source pipeline of QIIME. Thus, we have to rearrange the data to facilitate analysis according to the following steps:
Step 1. Delete unclassified genomic segments.
Step 2. Calculate the ratios of the remaining entries.
Step 3. Select the most abundant bacteria within soil samples to observe
their distribution using the following bar charts (Fig. 3).
After making the Stacked bar, we organized the data to facilitate the analysis (Table 1). We observed growths and declines of bacteria in the soil, and then summarized the bacteria according to their functions. Moreover, we will explain what we do in our farm affect microbiota. For example: Sphingomonas, Alcanivorax, Devosia which are polluting indicator bacteria, are decreased continuously from May to July. We speculate that the reason of their decline is that we have not applied herbicides, pesticides or other pollutants in these three months. When the soil repaired by itself, the pollutants are falling, and the polluting indicator bacteria are also falling. After the series of analysis, we hope to prove the basic hypothesis of our project--we can precisely regulate microbiota in the soil by using bio-stimulator. We will put our details of analysis in our demonstration.
Figure 3: Stacked bar plot of top-20 bacteria ratio in different samples
Spearman's Rank Correlation
The strength of co-occurrence of bacteria within soil samples was evaluated by the Spearman’s rank correlation coefficients. It ranges from -1 to 1. The formula of Spearman correlation coefficient is as follows:
$$\rho_s=1-\frac{6\sum d_{i^2}}{n(n^2-1)}$$
Symbol |
Unit |
Explanation |
|---|---|---|
| $\rho_s$ | - | Spearman's correlation value |
| $d_i$ | - | The difference in the ranked observations from each group |
| $n$ | - | The sample size |
Then we use heat maps to visualize the correlation strengths. Figure 4 shows the 20 most abundant bacteria within soil samples.
A computer program can then visualize the results in a heat map. A map of the 20 most abundant bacteria of our soil is shown below:
Figure 4: June : Correlation table of top-20 bacteria
Alpha-Diversity Analysis
Use of bio-stimulators to manipulate soil factors requires careful consideration of the microbiota. Certain stimulators may cause specific genera of bacteria to become overly dominant, damaging soil integrity. As a method of monitoring the balance of the microbial ecosystem, we investigate the evenness of the soil.
Eveness--Shannon Index
Microbial diversity is measured by alpha-diversity (α-diversity). In our study, α-diversity refers richness and the Shannon diversity index. Richness means the number of OTUs, and evenness of bacterial community is measured by the Shannon diversity index, as shown below:
$$H'=-\sum_{i=1}^{S}p_{i}lnp_i$$
Symbol |
Unit |
Explanation |
|---|---|---|
| $H'$ | - | Shannon index |
| $S$ | - | The total number of genuses in samples |
| $p_i$ | - | The ratio of bacteria amount of the ith genus in the sample |
A higher Shannon index indicates greater evenness. The estimated degree of evenness can be derived from the exponential of the value. For example, a soil sample with Shannon index 2.85 and $$e^{2.85}=17$$ It means that the sample approximately consists of 17bacteria that are equal in numbers. Thus, the Shannon index can be used as an observational tool to determine whether bio-stimulators decrease the overall evenness or not, and thus health and stability, of the soil.
Triplicate Analysis
Figure 5: The box plot of shannon index triplicate analysis
References
1. Kumar, A. and L. C. Rai (2017). "Soil Organic Carbon and Availability of Soil Phosphorus Regulate Abundance of Culturable Phosphate Solubilizing Bacteria in Paddy Fields of the Indo-Gangetic Plain." Pedosphere.
2. Wang, P., et al. (2015). "Long-term rice cultivation stabilizes soil organic carbon and promotes soil microbial activity in a salt marsh derived soil chronosequence." Scientific Reports 5: 15704.
