Microbiome based identification, monitoring and enhancement of fermentation processes and products

Abstract

Monitoring, analysis and control of fermentation activities includes methods and corresponding systems directed toward agriculture, biofuels, and food production. Complex methods and corresponding systems are provided for classifying a microorganism; profiling a microbiome; sequencing multiple libraries in a single sequencing run; determining a microbiome profile in a sample; and analyzing a material from a location associated with a fermentation process. Additional implementations are directed to methods and corresponding systems for obtaining, deriving, predicting and evaluating microbiome information; control, analysis and direction of fermentation operations; and evaluating, analyzing and displaying microbiome related information in two and three dimensional plots. Yet additional methods and corresponding systems permit identification and analysis of microorganisms capable of imparting beneficial properties to phases of fermentation processes.

What is claimed is: 
     
         1 . A method comprising:
 receiving a set of samples comprising agricultural material;   extracting nucleic acid material from each of the set of samples, with use of a bead-beating homogenization process;   barcoding said nucleic acid material with a barcoding process configured to correct sequencing errors and detect multi-bit errors and increase sequencing depth performance;   amplifying said nucleic acid material in coordination with said barcoding, in order to generate a 16S library and an internal transcribed spacer (ITS) library from said nucleic acid material;   pooling material of the 16S library with material of the ITS library to generate a pooled library;   sequencing the pooled library within a single run of a high-throughput sequencer, thereby obtaining a set of nucleic acid sequence reads of 16S and ITS genes of microorganisms represented in the set of samples; and   generating one or more clusters upon clustering reads of the set of nucleic acid sequence reads; and   selecting a representative sequence from each of the one or more clusters to return a characterization of bacterial and fungal microorganism abundances for each of the set of samples.   
     
     
         2 . The method of  claim 1 , further comprising returning a report characterizing a microbiome profile from the characterization. 
     
     
         3 . The method of  claim 1 , further comprising returning a personalized product prescription from the characterization, the personalized product prescription configured to improve production of an agricultural product associated with the set of samples. 
     
     
         4 . The method of  claim 3 , wherein the personalized product prescription is configured to improve characteristics of at least one of nitrogen fixation and carbon fixation in an environment associated with the set of samples. 
     
     
         5 . The method of  claim 3 , wherein the agricultural product comprises at least one of: a sugar crop and a starch-producing crop. 
     
     
         6 . The method of  claim 1 , wherein said agricultural material comprises soil. 
     
     
         7 . The method of  claim 1 , wherein said agricultural material comprises a plant part comprising at least one of: a leaf part, a stem part, a root part, and a seed. 
     
     
         8 . The method of  claim 1 , wherein said agricultural material comprises material of a food production process. 
     
     
         9 . The method of  claim 1 , wherein said characterization of bacterial and fungal microorganism abundances comprises characterizing microorganisms from the group consisting essentially of: a single-celled organism, a bacteria, an archaea, a protozoan, a unicellular fungus and a protist. 
     
     
         10 . The method of  claim 1 , wherein clustering reads of the set of nucleic acid sequence reads comprises clustering sequences exhibiting a threshold level of similarity, and selecting a representative sequence for each cluster for taxonomic assignment. 
     
     
         11 . The method of  claim 1 , wherein said sequencing comprises employing a long-read sequencing platform. 
     
     
         12 . The method of  claim 1 , wherein receiving the set of samples comprises providing a kit comprising containers for sample reception, reagents for sample reception, and instructions for method performance executable by way of a computer-readable medium. 
     
     
         13 . The method of  claim 1 , wherein returning the characterization comprises returning the characterization based upon the representative sequences and auxiliary data comprising: geographical information and climate. 
     
     
         14 . The method of  claim 1 , wherein the barcoding process comprises a double-index barcoding process implementing tagging with a first class of Hamming codes and a second class of Golay codes. 
     
     
         15 . A method comprising:
 a) receiving a set of samples comprising agricultural material;   b) extracting nucleic acid material from each of the set of samples, with use of a homogenization process;   c) barcoding said nucleic acid material with a barcoding process configured to correct sequencing errors and detect multi-bit errors and increase sequencing depth performance;   d) amplifying said extracted and barcoded nucleic acid material to generate a 16S library and an internal transcribed spacer (ITS) library from said nucleic acid material;   e) pooling the material of the 16S library with the material of the ITS library to generate a pooled library;   f) sequencing the pooled library within a single run of a high-throughput sequencer, thereby obtaining a set of nucleic acid sequence reads of 16S and ITS genes of microorganisms represented in the set of samples; and   g) applying the set of sequence reads representing microorganisms in each sample to a trained machine learning model, wherein the trained machine learning model comprises network architecture and regression architecture and is trained on 16S and ITS sequence data associated with soil status, agriculture product quality, contamination, and treatment response,   the output of the trained machine learning model providing characterization of bacterial and fungal microorganism abundances for each of the set of samples.   
     
     
         16 . The method of  claim 15 , wherein said agricultural material comprises at least one of: soil, a leaf part, a stem part, a root part, and a seed. 
     
     
         17 . The method of  claim 15 , wherein the trained machine learning model comprises classification architecture for classification of soil nutrients associated with the set of samples. 
     
     
         18 . The method of  claim 15 , wherein the trained machine learning model comprises classification architecture for classification of crop disease states associated with the set of samples 
     
     
         19 . The method of  claim 15 , wherein the trained machine learning model comprises at least one of support vector machine architecture and deep-layered belief nets architecture. 
     
     
         20 . The method of  claim 15 , wherein the output characterizes a microbiome profile for the set of samples and provides a personalized product prescription configured to improve production of an agricultural product associated with the set of samples.