Authors would like to thank Andrew Bird for the silage samples and Audrey Farbos of the Exeter Sequencing Service for her assistance in preparing DNA sequencing libraries. Exeter Sequencing Service and Computational core facilities at the University of Exeter. Medical Research Council Clinical Infrastructure award (MR/M008924/1). Wellcome Trust Institutional Strategic Support Fund (WT097835MF), Wellcome Trust Multi User Equipment Award (WT101650MA) and BBSRC LOLA award (BB/K003240/1).

1. Site Location
<ol>
	<li>Collect the silage sample from an appropriate site such as a farm. Here, the farm was located in Ballydulea, Co. Cork, Ireland (51&#176;51&#39;58.4&#34;N 8&#176;16&#39;48.7&#34;W).</li>
</ol>
2. DNA Extraction
NOTE: DNA extraction was performed using a commercial kit following the manufacturer&#39;s instructions. A negative control, which contained no sample, was used throughout the library preparation method.
<ol>
	<li>Add 100 - 400 mg of sample to 978 &#181;L sodium phosphate buffer and 122 &#181;L soil lysis buffer in the supplied lysis tubes.</li>
	<li>Homogenize samples by placing the lysis tubes into the homogenizer for 40 s at a speed of 6.0 m/s.</li>
	<li>Centrifuge lysates at 14,000 x g for 15 min and transfer the supernatant to a clean micro-centrifuge tube containing 250 &#181;L of Protein Precipitate Solution (PPS). Mix the solution by inverting 10 times and centrifuge at 14,000 x g for 5 min.</li>
	<li>Add the supernatant to 1 mL DNA binding matrix in a clean 15 mL centrifuge tube. Mix the solution by inverting the tube constantly for 3 min. Allow the mixture to settle for 3 min, then discard 500 &#181;L of supernatant. Mix the remaining supernatant.</li>
	<li>Transfer 600 &#181;L of the suspension to a spin filter and centrifuge at 14,000 x g for 1 min. Discard the filtrate and repeat the process with the remaining suspension.</li>
	<li>Add 500 &#181;L of wash buffer to the DNA binding matrix within the spin filter, mix by pipetting, then centrifuge at 14,000 x g for 1 min.</li>
	<li>Discard the filtrate and centrifuge the spin filter again at 14,000 x g for 2 min to ensure all wash buffer is removed. Dry the spin filter at 23 &#730;C for 5 min.</li>
	<li>Pre-warm (70 &#730;C) the DNase-free water (DES) and re-suspend the DNA binding matrix in 100 &#181;L of DES within the spin filter. Transfer the spin filter to a clean 1.5 mL micro-centrifuge tube and centrifuge at 14,000 x g for 1 min to elute DNA. Store the purified DNA at -20 &#730;C until further analysis is performed.</li>
</ol>
3. DNA Purification Using DNA Purification Beads
NOTE: Prior to metagenomic library preparation the extracted DNA was purified using purification beads to ensure a pure DNA sample was obtained.
<ol>
	<li>Incubate the beads at 23 &#730;C for 30 min before use. Add 2 volumes of beads to the DNA sample and incubate the solution at 23 &#730;C for 5 min.</li>
	<li>Place the samples onto a separation magnet for 5 min and then discard the supernatant. Wash the beads twice with 200 &#181;L fresh 80% ethanol (EtOH). Air dry the beads for 10 min.</li>
	<li>Remove the samples from the separation magnet and add 50 &#181;L of elution buffer (EB), mix by pipetting.</li>
	<li>Incubate the suspension at 23 &#730;C for 5 min, after which place the samples back onto the separation magnet for 3 min.</li>
	<li>Transfer the supernatant, which contains the DNA, to a clean tube. Discard the beads.</li>
	<li>Quantify the purified DNA as per section four.</li>
</ol>
4. Quantification of Purified DNA
NOTE: Purified DNA was quantified using a fluorometer and double-stranded (dsDNA) High Sensitivity (HS) assay kit following the manufacturer&#39;s instructions.
<ol>
	<li>Prepare a working solution using 199:1 ratio of buffer to reagent.</li>
	<li>Add 10 &#181;L of each DNA standard to 190 &#181;L of working solution.</li>
	<li>Add 10 &#181;L of purified DNA to 190 &#181;L of working solution. The final volume should be 200 &#181;L. Incubate standard and DNA samples at 23 &#730;C for 2 min.</li>
	<li>Analyze standards before the DNA samples on the fluorometer using the on-screen instructions.</li>
</ol>
5. Shotgun Sequencing Library Preparation
NOTE: The shotgun sequencing library was prepared using a commercial library preparation kit using the manufacturer&#39;s instructions.
<ol>
	<li>Dilute the DNA samples to 0.2 ng/&#181;L using EB. Any sample which is already below this concentration, i.e. the negative control, is left at its current concentration.</li>
	<li>Mix 5 &#181;L of the purified DNA with 10 &#181;L buffer and 5 &#181;L enzyme mix. Incubate samples at 55 &#730;C for 5 min.</li>
	<li>Add 5 &#181;L of neutralizing buffer and incubate the solution at 23 &#730;C for 5 min.</li>
	<li>Add 5 &#181;L of each of the sample specific sequencing indices and 15 &#181;L of PCR master mix.</li>
	<li>In a thermocycler, incubate the samples at 72 &#730;C for 3 min, 95 &#730;C for 30 s, before 12 cycles of 95 &#730;C for 10 s, 55 &#730;C for 30 s and 72 &#730;C for 30 s. Incubate samples finally at 72 &#730;C for 5 min.</li>
	<li>Purify the prepared DNA using the bead purification as before but with a final elution of 30 &#181;L of EB.</li>
</ol>
6. Library Quantity and Quality Check
NOTE: The quantity and quality of the prepared libraries were assessed using a commercial kit and instrumentation.
<ol>
	<li>Incubate the kit components at 23 &#730;C for 30 min prior to use.</li>
	<li>Add 2 &#181;L of DNA to 2 &#181;L of buffer and vortex for 1 min at 2,000 rpm.</li>
	<li>Spin down the sample to ensure it is at the bottom of the tube.</li>
	<li>Insert the sample tubes, analysis tape and tips into the instrument, and perform analysis as directed by the software.</li>
</ol>
7. DNA Sequencing
<ol>
	<li>Transfer the prepared and quantified DNA sequencing libraries samples to a sequencing service and sequence using 300 bp paired end sequencing 45.</li>
</ol>
8. Analysis of Raw Sequence Data
NOTE: The commands for each program using a Linux operating system are shown below the protocol step. The pipeline used for sequence data analysis is shown in Figure 1. The programs are to be installed by the user prior to analysis. This process should be performed individually for each sample.
<ol>
	<li>Analyze and visualize DNA sequence data using FastQC 46 by typing in to the command line /path-to-file/fastqc, followed by the forward and reverse raw reads raw_read1.fastq raw_read2.fastq.</li>
	<li>Specify an output folder by typing -o output_fastqc and the file format of the raw read files by -f fastq.</li>
	<li>View the output file (Figure 2). 
	path-to-file/fastqc raw_read1.fastq raw_read2.fastq -o output_directory -f fastq.</li>
</ol>
9. Quality Control Trimming and Filtering Sequence Data
<ol>
	<li>Run the trimming program, Trimmomatic 28 by typing into the command line java -jar /path-to-file/ trimmomatic-0.35.jar.</li>
	<li>Specify the files are paired end files by typing &#39;PE&#39;. State that 16 central processing units (CPUs) should be used by the program by typing -threads 16.</li>
	<li>List the two files to QC check by typing the names of the raw forward and reverse reads. The prefix of the output files is determined by typing -baseout silage.</li>
	<li>Define the options for the program by typing ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 CROP:200 HEADCROP:15 MINLEN:36.</li>
	<li>Once complete, analyze the trimmed sequences using FastQC as before and compare the output to the raw sequence data to ensure trimming has been performed successfully. 
	NOTE: The software tool, Trimmomatic, trimmed reads further by removing leading low quality or N bases (below quality 3), removing trailing low quality or N bases (below quality 3) and scanning each read with a 4-base wide sliding window. The parameters were set for cutting when the average quality per base drops below 20 and then to drop any reads below 36 bases long. Finally, 15 bases were cropped from the head of each read and reads were cropped to keep 200 bases from the start of the read. This final step was performed to overcome some quality issues when sequencing long (&#62; 200 bp) reads. These can be adjusted for specific samples 28. 
	java -jar /path-to-file/trimmomatic-0.35.jar PE -threads 16 raw_read1.fastq raw_read2.fastq -baseout silage ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 CROP:200 HEADCROP:15 MINLEN:36</li>
</ol>
10. Metagenome Assembly
<ol>
	<li>Merge the unpaired, trimmed reads by typing cat followed by the unpaired reads; silage_read1_unpaired.fastq silage_read2_unpaired.fastq. Write the files to a new file by typing &#62; silage_merged_unpaired.fastq 
	cat silage_read1_unpaired.fastq silage_read2_unpaired.fastq &#62; silage_merged_unpaired.fastq</li>
	<li>To de novo assemble the sequenced DNA, use SPAdes (St. Petersburg genome assembler) 30 by typing /path-to-file/spades.py. Specify that 16 CPUs are to be used by typing -t 16 and that the metagenomic parameter should applied by typing --meta.</li>
	<li>Identify the trimmed forward reads using -1 silage_read1_paired.fastq and the reverse reads by -2 silage_read2_paired.fastq. The merged unpaired reads are specified by -s silage_merged_unpaired.fastq.</li>
	<li>Define the output folder by typing -o silage_spades. 
	path-to-file/spades.py -t 16 --meta -1 silage_read1_paired.fastq -2 silage_read2_paired.fastq -s silage_merged_unpaired.fastq -o silage_spades</li>
</ol>
11. Paired-end Read Overlap
<ol>
	<li>Merge pairs of DNA sequence reads using FLASH (Fast Length Adjustment of Short Reads) 29 by typing into command line /path-to-file/flash. Specify that 16 CPUs should be used by using -t 16 and the output prefix by typing -o silage.</li>
	<li>Identify trimmed reads by typing silage_trimmed_R1.fastq silage_trimmed_R2.fastq 
	path-to-file/flash -t 16 -o FLASHed silage_read1_paired.fastq silage_read2_paired.fastq</li>
</ol>
12. Taxonomic Classification
<ol>
	<li>Type /path-to-file/kraken and specify the database by typing --db /path-to-file/standard.</li>
	<li>Define that 16 CPUs should be used by typing --threads 16 and identify an output folder by using --output FLASHed_silage_extendedFrags_kraken.txt. Type the input file name; FLASHed_silage.extendedFrags.fastq 
	path-to-file/kraken --db standard --thread 16 --output FLASHed_silage_extendedFrags_kraken.txt FLASHed_silage.extendedFrags.fastq 
	NOTE: Classification of the assembled DNA sequence scaffolds using Kraken 7 was completed against the most recent, standard Kraken database that contained all available Prokaryote genome sequences.</li>
	<li>Transfer columns 2 and 3 from the output file and to a new file by typing cut -f2,3 FLASHed_silage_extendedFrags_kraken.txt &#62; FLASHed_silage_extendedFrags_kraken.int</li>
	<li>Open the output file in web browser. 
	cut -f2,3 FLASHed_silage_extendedFrags_kraken.txt &#62; FLASHed_silage_extendedFrags_kraken.int</li>
	<li>Import the new file into Krona 12 by typing ktImportTaxonomy. Specify the input file by typing FLASHed_silage_extendedFrags_kraken.int. Identify the output file by typing -o FLASHed_silage_extendedFrags_kraken.out.html. 
	path-to-file/ktImportTaxonomy FLASHed_silage_extendedFrags_kraken.int -o FLASHed_silage_extendedFrags_kraken.out.html</li>
</ol>
13. Functional Annotation
<ol>
	<li>Go to the MG-RAST 47 website, http://metagenomics.anl.gov/. Register as a new user if required. After logging in, Click on the &#8220;Upload&#8221; button. Upload the assembled scaffolds from Step 10.</li>
	<li>Once the files have uploaded, click on &#34;Submit&#34; and follow the instructions and await the completion of analysis.</li>
	<li>After the analysis is complete, view the link sent via email from MG-RAST, or alternatively, click on &#34;Progress&#34;. There is a list of completed jobs. Click on the relevant job id and then on the link to the &#34;download page&#34;.</li>
	<li>On the download page, under the heading &#34;Protein Clustering 90%&#34;, click on the protein button to download the predicted protein file, 550.cluster.aa90.faa.</li>
	<li>To classify the proteins as putatively belonging to a particular CAZy enzyme class, compare the downloaded proteins to the CAZy database 48. Download the Carbohydrate-Active enZYmes Database (CAZy) from files are: AA.zip, CE.zip, GH.zip, GT.zip and PL.zip. These files represent the following enzyme classes respectively: Auxiliary Activities (AA), Carbohydrate Esterases (CE), Glycoside Hydrolases (GH), Glycosyl Transferases (GT) and Polysaccharide Lyases (PL).</li>
	<li>Unzip the database files and annotate the proteins by determining the protein similarity to the CAZy database proteins using the USEARCH UBLAST algorithm 49. To use a bash loop (for i in *.txt) to iterate through the 5 database .txt files type &#34;for i in *.txt; do&#34;.</li>
	<li>Run USEARCH by typing /path-to-file/usearch8 with the parameter -ublast in order to use the ublast algorithm. Then type in the name of the protein sequence file downloaded from MG-RAST, &#34;mgmXXXXXX.3.550.cluster.aa90.faa&#34;.</li>
	<li>To indicate the database file to be used type &#34;-db $i&#34; and to specify the E-value threshold at 1e-5, type &#34;-evalue 1e-5&#34;.</li>
	<li>To terminate the search after the discovery of a target sequence and therefore classifying that protein sequence as belonging to the target enzyme class, e.g. GH, type &#34;-masaccepts 1&#34;.</li>
	<li>To define that 16 CPUs should be used type &#34;-threads 16&#34; and to specify the format of the output file as atab-separated text type &#34;-blast6out&#34;. To identify the output file type &#34;$i.ublast&#34;. To terminate the bash loop, type &#34;; done&#34; 
	for i in *.txt; 
	do /path-to-file/usearch8 -ublast ../mgmXXXXXX.3.550.cluster.aa90.faa -db $i -evalue 1e-5 -maxaccepts 1 -threads 16 -blast6out $i.ublast; 
	done</li>
</ol>
14. Visualizing CAZy Annotation
<ol>
	<li>To visualize the output from the CAZy annotation as a Venn diagram, generate protein ID lists for each enzyme class using a bash loop. Type &#34;for i in *.ublast; do&#34;.</li>
	<li>To transfer column 1 from the output file and to a new file, type &#34;cat $i | cut -f 1 &#62;$i.list&#34;.</li>
	<li>Terminate the loop and type &#34;; done&#34;.</li>
	<li>Open the .list files in a text editor. Go to the website , select the number of sets as 5 and paste the content of each list file in a separate box. Download the resulting diagram as a .SVG file. 
	for i in *.ublast; 
	do cat $i | cut -f 1 &#62;$i.list; 
	done</li>
</ol>

The authors have nothing to disclose.

While an in silico analysis can give an excellent insight to the microbial communities that are present within environmental samples, it is critical that the taxonomic classifications demonstrated be performed in association with relevant controls and that a suitable depth of sequencing has been achieved to capture the entire population present 51.
With any computational analysis, there are many routes to achieve a similar goal. The methods that we have used in this study are examples of suitable and straightforward methods, that have been brought together to achieve a range of analyses on the silage microbiome. A variety and an ever-increasing number of bioinformatics tools and techniques are available to analyze metagenomic data, for instance Phylosift 8 and MetaPhlAn2 52, and these should be evaluated prior to the investigation for their relevance to the sample and the analysis required 53. Metagenomic analysis methods are limited by the databases for available for classification, sequencing depth and the quality of sequencing.
The bioinformatic processing demonstrated here was performed on a local, high powered machine; however cloud-based systems are also available. These cloud-based services allow for the rental of the necessary computational power without having the high-cost investment of a suitable powerful local workstation. A potential application of this method would be to assess silage before its use in agriculture to ensure that no potentially harmful bacteria are present therefore preventing them from entering the food chain.

Metagenomics is the direct analysis of DNA purified from biological communities found within environmental samples 1 and was originally used to detect unculturable bacteria found in sediments 2. Metagenomics has been widely used for a number of applications, such as identifying the human microbiome 3, classifying microbial populations within the ocean 4 and even for the analysis of the bacterial communities that develop on coffee machines 5. The introduction of next generation sequencing technologies resulted in greater sequencing throughput and output. Consequently, DNA sequencing has become more economical 6 and the depth of sequencing that can be performed has greatly increased, enabling metagenomics to become a powerful, analytical tool.
&#34;Front-end&#34; enhancements in the practical, molecular aspect of metagenomic sequencing have driven the growth of the in silico bioinformatics tools available for the taxonomic classification 7-9, functional annotation 10,11 and visual representation 12,13 of DNA sequence data. The increasing number of available, sequenced prokaryotic and eukaryotic 14 genomes allows further accuracy in the classification of microbial communities, which are invariably performed against a &#34;back-end&#34; reference database of sequenced genomes 15. Two main approaches can be adopted for metagenomic analysis.
The more conventional method is analysis of the 16S rRNA gene coding region of bacterial genome. The 16S rRNA is highly conserved between prokaryote species but exhibits nine hyper-variable regions (V1 - V9) which can be exploited for species identification 16. The introduction of longer sequencing (&#8804; 300 bp paired end) allowed for the analysis of DNA sequences spanning two hyper-variable regions, in particular the V3 - V4 region 17. Advances in other sequencing technologies, such as Oxford Nanopore 18 and PacBIO 19, do allow the entire 16S rRNA gene to be sequenced contiguously.
While 16S rDNA based libraries provide a targeted approach to species identification and enable the detection of low copy number DNA that naturally occurs within purified samples, shotgun sequencing libraries allow for the detection of species that may contain DNA regions that are either not amplifiable by the 16S rRNA marker primer sequences used, or because the differences between the template sequence and the amplifying primer sequence are too great 20,21. Furthermore, although DNA polymerases have a high fidelity of DNA replication, base errors can nonetheless occur during PCR amplification and these incorporated errors can result in incorrect classification of originating species 22. Biases in the PCR amplification of template sequences can also occur; sequences of DNA with a high GC content can be under represented in the final amplicon pool 23 and similarly unnatural base modifications, such as thymine glycol, can halt DNA polymerases causing failures in the amplification of DNA sequences 24. In contrast, a shotgun sequencing DNA library is a DNA library that has been prepared by using all of the purified DNA that has been extracted from a sample and subsequently fragmented into shorter DNA chain lengths prior to preparation for sequencing. Taxonomic classification of DNA sequences generated by shotgun sequencing is more accurate when compared to 16S rRNA amplicon sequencing 25, although the financial cost required to reach a reliable sequencing depth is greater than that of amplicon sequencing 26. The major benefit of shotgun sequencing metagenomics is that sequenced regions of the various genomes in the sample are available for gene prospecting once they have been has been taxonomically classified 27.
Metagenomic sequence data is analyzed by an ever-increasing range of bioinformatic tools. These tools are able to perform a wide variety of applications, for example, quality control analysis of the raw sequence data 28, overlapping of paired end reads 29, de novo assembly of sequence reads to contigs and scaffolds 30,31, taxonomic classification and visualization of sequence reads and assembled sequences 7,12,32,33 and the functional annotation of assembled sequences 34,35.
Silage, produced by farmers throughout the world from fermented cereals such as maize (Zea mays), is predominately used as cattle feed. Silage is treated with the bacterium Lactobacillus sp. to aid fermentation 36 but to date, there is limited knowledge of the other microbial populations found in silage. The fermentation process can lead to undesirable and potentially harmful micro-organisms becoming prevalent within the silage 37. In addition to yeasts and molds, bacteria are particularly adaptable to the anaerobic environment in fermenting silage and are more frequently associated with diseases in livestock rather than the degradation of the silage 38. Butyric acid bacteria can be inadvertently added from soil remains when filling the silage silos and are able to convert the lactic acid, a product of anaerobic digestion, to butyric acid, thus increasing the pH of the silage 39. This increase in pH can lead to an upsurge in spoilage bacteria that would normally be unable to sustain growth under optimum silage fermentation conditions 38. Clostridium spp., Listeria spp. and Bacillus spp. are of particular concern, especially in silage for dairy cattle feed, as bacterial spores that have survived the gastrointestinal tract 40 can enter the food-chain, lead to food spoilage and, in rare cases, to animal and human fatalities 37,39,41-44. Moreover, while it is difficult to estimate the exact economic impact of veterinary treatment and livestock loss caused by silage spoilage, it is likely to be detrimental to a farm if an outbreak was to occur.
It is hypothesized that by using a metagenomic approach we can classify the microbial populations that are present in silage samples and furthermore identify microbial communities associated with silage spoilage that would, in turn, potentially have a detrimental effect on the livestock, enabling remedial action to be taken before the silage is to be used as a food source.

<table border="0" cellpadding="0" cellspacing="0" width="765">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">FastDNA SPIN Kit for Soil</td>
			<td style="width:128px;">MP Bio</td>
			<td style="width:120px;">116560200</td>
			<td style="width:301px;">DNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DNA FastPrep</td>
			<td style="width:128px;">MP Bio</td>
			<td style="width:120px;">116004500</td>
			<td>DNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Agencourt AMPure XP beads</td>
			<td style="width:128px;">Beckman Coulter</td>
			<td style="width:120px;">A63880</td>
			<td>DNA Purification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Elution Buffer</td>
			<td style="width:128px;">Qiagen</td>
			<td style="width:120px;">19806</td>
			<td>DNA Purification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit Fluorometer</td>
			<td style="width:128px;">Thermo Fisher</td>
			<td style="width:120px;">Q33216</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qubit dsDNA HS Assay Kit</td>
			<td style="width:128px;">Thermo Fisher</td>
			<td style="width:120px;">Q32854</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Nextera XT DNA Library Prep Kit</td>
			<td style="width:128px;">Illumina</td>
			<td style="width:120px;">FC-131-1024</td>
			<td>Library Preparation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Nextera XT Index Kit</td>
			<td style="width:128px;">Illumina</td>
			<td style="width:120px;">FC-131-1001</td>
			<td>Library Preparation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">TapeStation 2200</td>
			<td style="width:128px;">Agilent</td>
			<td style="width:120px;">G2964AA</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">HS D100 ScreenTape</td>
			<td style="width:128px;">Agilent</td>
			<td style="width:120px;">5067-5584</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">HS D100 ScreenTape Reagents</td>
			<td style="width:128px;">Agilent</td>
			<td style="width:120px;">5067-5585</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">TapeStation Tips</td>
			<td style="width:128px;">Agilent</td>
			<td style="width:120px;">5067-5153</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">TapeStation Tubes</td>
			<td style="width:128px;">Agilent</td>
			<td style="width:120px;">401428 and 401425</td>
			<td>DNA Quantification</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">HiSeq 2500</td>
			<td style="width:128px;">Illumina</td>
			<td style="width:120px;"></td>
			<td>DNA Sequencing - provided by a sequencing service</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">High Power Analysis Workstation</td>
			<td style="width:128px;">Various</td>
			<td style="width:120px;"></td>
			<td>Local or cloud based, user preferred system</td>
		</tr>
	</tbody>
</table>

metagenomic analysis of silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Prior to bioinformatic processing, raw sequence reads were trimmed and adapters were removed using Trimmomatic software 28. After the trimming and filtering step, the number of reads was reduced to 50% of the sequence reads (Table 1). The average base phred score was &#62;30 after quality control (Figure 2).
Pairs of DNA sequences which had overlapping regions were merged using FLASH software 29 to generate single longer reads, non-overlapping reads were kept in a separate file. 45.47% reads (105,343) combined successfully. Following the overlapping of reads using FLASH of reads, the resulting extended fragments underwent bacterial taxonomic classification using Kraken software 7 and were subsequently visualized with Krona software (Figure 3).
The majority of the bacterial species present in the silage metagenome are found within 4 prokaryotic phyla: Firmicutes (34%), Actinobacteria (28%), Proteobacteria (27%) and Bacteroidetes (7%). The distribution of classes present within these phyla can be seen in Figure 4. The most abundant species in the metagenome were Lactobacillus spp. (24%; Firmicutes), Corynebacterium spp. (8%; Actinobacteria), Propionibacterium spp. (3%; Actinobacteria) and Prevotella spp. (3%; Bacteroidetes). Species important to animal health and implicated in disease were also observed; Clostridium spp. (1%) Bacillus spp. (0.6%), Listeria spp. (0.2%) were predicted to be present in the silage sample.
Functional annotation was performed on assembled reads. The metagenome was assembled using the SPAdes assembler 30 using the trimmed and filtered paired-end and unpaired reads generating 92,284 scaffolds. In order to identify cellulases, proteins were predicted using MG-RAST and annotated using the Carbohydrate-Active enZYmes Database (CAZy). Of the 97,562 predicted proteins, 6357 were annotated as a putative carbohydrate-active enzyme in one of the five enzymes classes that make up the CAZy database (Figure 5). Results were visualized as a Venn diagram using InteractiVenn software 50 showing the distribution of protein annotations including those containing more than one CAZy enzyme class annotation. Of these, 3861 were predicted to have glycoside hydrolase activity and will be further characterized in the laboratory to confirm function.
<img alt="Figure 1" src="/files/ftp_upload/54936/54936fig1.jpg" /> 
Figure 1: Bioinformatic Metagenomics Pipeline for the Analysis of Silage. Two main approaches were used to investigate the microbiome of silage, taxonomic classification and functional annotation. <a href="http://cloudflare.jove.com/files/ftp_upload/54936/54936fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54936/54936fig2.jpg" /> 
Figure 2: Sequence Quality Per-base Before and After Trimming and Adapter Removal. The per-base sequence quality plot from FASTQC shows the average phred score across the length of the sequence reads pre- and post- quality control. <a href="http://cloudflare.jove.com/files/ftp_upload/54936/54936fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54936/54936fig3.jpg" /> 
Figure 3: Taxonomic Classification of the Bacterial Microbiome of Solid Silage. Classification of trimmed and overlapping sequence reads from FLASH was performed using Kraken 7 and subsequently visualized with krona. <a href="http://cloudflare.jove.com/files/ftp_upload/54936/54936fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54936/54936fig4.jpg" /> 
Figure 4: Taxonomic Class Distribution of the 4 Most Abundant Phyla in the Bacterial Microbiome of Solid Silage. The percentage of each class of bacteria within the four most abundant phyla. Firmicutes: Clostridia (red) and Bacilli (dark blue); Proteobacteria: delta/epsilon (pink), alpha (pale blue), gamma (orange) and beta (turquoise); Bacteroidetes: Flavobacteriia (dark blue) and Bacteroidia (pale green); Actinobacteria: Coriobacteriia (dark purple) and other Actinobacteria (dark green). <a href="http://cloudflare.jove.com/files/ftp_upload/54936/54936fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54936/54936fig5.jpg" /> 
Figure 5: CAZy Annotation of the Predicted Proteome in the Solid Silage Microbiome. Venn diagram showing the distribution of the five enzyme classes of CAZy annotations in the predicted proteome of solid silage microbiome. <a href="http://cloudflare.jove.com/files/ftp_upload/54936/54936fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td># Raw reads</td>
			<td rowspan="2"># Filtered reads (paired)</td>
			<td># Filtered reads</td>
			<td rowspan="2"># FLASHed reads</td>
		</tr>
		<tr>
			<td>(paired)</td>
			<td>(unpaired)</td>
		</tr>
		<tr>
			<td>2,374,949 x2</td>
			<td>231,679 x2</td>
			<td>1,892,534</td>
			<td>105,343</td>
		</tr>
	</tbody>
</table>
Table 1: Summary Table of Sequencing Reads.

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Metagenomic Analysis of Silage

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

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Genetics

18.0K Views.  University of Exeter. Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Video: Metagenomic Analysis of Silage

Sequential Salt Extractions for the Analysis of Bulk Chromatin Binding Properties of Chromatin Modifying Complexes

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous nature of most mammalian chromatin-modifying complexes. In order to overcome these hurdles, we have adapted a sequential salt extraction (SSE) assay for evaluating the relative binding affinities of chromatin-bound complexes. This easy and straightforward assay can be used by non-experts to evaluate the relative difference in binding affinity of two related complexes, the changes in affinity of a complex when a subunit is lost or an individual domain is inactivated, and the change in binding affinity after alterations to the chromatin landscape. By sequentially re-suspending bulk chromatin in increasing amounts of salt, we are able to profile the elution of a particular protein from chromatin. Using these profiles, we are able to determine how alterations in a chromatin-modifying complex or alterations to the chromatin environment affect binding interactions. Coupling SSE with other in vitro and in vivo assays, we can determine the roles of individual domains and proteins on the functionality of a complex in a variety of chromatin environments.

Sequential salt extraction of chromatin bound proteins is a useful tool for determining the binding properties of large protein complexes. This method can be employed to evaluate the role of individual subunits or domains in the overall affinity of a protein complex to bulk chromatin.

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous ...

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on (TRO) Approach

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here is a powerful experimental approach for analyzing the transcription termination defect under physiological conditions. Like the traditional TRO assay, it relies on the presence of a transcriptionally active polymerase beyond the 3&#8242; end of the gene as an indicator of a transcription termination defect1. It overcomes two major problems encountered with the traditional TRO assay. First, it can detect if the polymerase reading through the termination signal is the one that initiated transcription from the promoter-proximal region, or if it is simply representing a pervasively transcribing polymerase that initiated non-specifically from somewhere in the body or the 3&#8242; end of the gene. Secondly, it can distinguish if the transcriptionally active polymerase signal beyond the terminator region is truly the readthrough sense mRNA transcribing polymerase or a terminator-initiated non-coding anti-sense RNA signal. Briefly, the protocol involves permeabilizing the exponentially growing yeast cells, allowing the transcripts that initiated in vivo to elongate in the presence of the BrUTP nucleotide, purifying BrUTP-labelled RNA by the affinity approach, reverse transcribing the purified nascent RNA and amplifying the cDNA using strand-specific primers flanking the promoter and the terminator regions of the gene2.

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on TRO Approach

We describe a basic experimental approach for analysis of termination of transcription by RNA polymerase II in vivo using BrUTP by the strand-specific transcription run-on (TRO) approach in budding yeast. This protocol can be extended to study transcription termination by other RNA polymerases both in yeast and higher eukaryotes.

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here ...

Single Molecule Analysis of Laser Localized Psoralen Adducts

The DNA Damage Response (DDR) has been extensively characterized in studies of double strand breaks (DSBs) induced by laser micro beam irradiation in live cells. The DDR to helix distorting covalent DNA modifications, including interstrand DNA crosslinks (ICLs), is not as well defined. We have studied the DDR stimulated by ICLs, localized by laser photoactivation of immunotagged psoralens, in the nuclei of live cells. In order to address fundamental questions about adduct distribution and replication fork encounters, we combined laser localization with two other technologies. DNA fibers are often used to display the progress of replication forks by immunofluorescence of nucleoside analogues incorporated during short pulses. Immunoquantum dots have been widely employed for single molecule imaging. In the new approach, DNA fibers from cells carrying laser localized ICLs are spread onto microscope slides. The tagged ICLs are displayed with immunoquantum dots and the inter-lesion distances determined. Replication fork collisions with ICLs can be visualized and different encounter patterns identified and quantitated.

Lasers are frequently used in studies of the cellular response to DNA damage. However, they generate lesions whose spacing, frequency, and collisions with replication forks are rarely characterized. Here, we describe an approach that enables the determination of these parameters with laser localized interstrand crosslinks.

The DNA Damage Response (DDR) has been extensively characterized in studies of double strand breaks (DSBs) induced by laser micro beam irradiation in live ...

Analysis of the c-KIT Ligand Promoter Using Chromatin Immunoprecipitation

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. Therefore, DNA-protein interactions regulate multiple physiological, pathophysiological, and biological functions, such as cell differentiation, cell proliferation, cell cycle control, chromosome stability, epigenetic gene regulation, and cell transformation. In eukaryotic cells, the DNA interacts with histone and nonhistone proteins and is condensed into chromatin. Several technical tools can be used to analyze DNA-protein interactions, such as the Electrophoresis (gel) Mobility Shift Assay (EMSA) and DNase I footprinting. However, these techniques analyze the protein-DNA interaction in vitro, not within the cellular context. Chromatin immunoprecipitation (ChIP) is a technique that captures proteins at their specific DNA binding sites, thereby allowing for the identification of DNA-protein interactions within their chromatin context. It is done by fixation&#160;of the DNA-protein interaction, followed by&#160;immunoprecipitation of the protein of interest. Subsequently, the genomic site that the protein was bound to is characterized. Here, we describe and discuss ChIP and demonstrate its analytical value for the identification of the Transforming Growth Factor-&#946; (TGF-&#946;)-induced binding of the transcription factor SMAD2 to SMAD Binding Elements (SBE) within the promoter region of the tyrosine-protein kinase Kit (c-KIT) receptor ligand Stem Cell Factor (SCF).

DNA-protein interactions are essential for multiple biological processes. During the evaluation of cellular functions, the analysis of DNA-protein interactions is indispensable for understanding gene regulation. Chromatin immunoprecipitation (ChIP) is a powerful tool to analyze such interactions in vivo.

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of enzymatic oxidation of 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) as well as detection of N6-methyladenine (6mA) in the DNA of multicellular organisms provided additional degrees of complexity to the epigenetic research. According to a growing body of experimental evidence, these novel DNA modifications may play specific roles in different cellular and developmental processes. Importantly, as some of these marks (e. g. 5hmC, 5fC and 5caC) exhibit tissue- and developmental stage-specific occurrence in vertebrates, immunochemistry represents an important tool allowing assessment of spatial distribution of DNA modifications in different biological contexts. Here the methods for computational analysis of DNA modifications visualized by immunostaining followed by confocal microscopy are described. Specifically, the generation of 2.5 dimension (2.5D) signal intensity plots, signal intensity profiles, quantification of staining intensity in multiple cells and determination of signal colocalization coefficients are shown. Collectively, these techniques may be operational in evaluating the levels and localization of these DNA modifications in the nucleus, contributing to elucidating their biological roles in metazoans.

Newly discovered oxidized forms of 5-methylcytosine (oxi-mCs), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) may represent distinct DNA modifications with unique functional roles. Here a semi-quantitative workflow for visualization of oxi-mCs' spatial distribution, signal intensity profiling and colocalization is described.

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Antibody-Free Assay for RNA Methyltransferase Activity Analysis

There are more than 100 chemically distinct modifications of RNA, two thirds of which consist of methylations. Interest in RNA modifications, and especially methylations, has re-emerged due to the important roles played by the enzymes that write and erase them in biological processes relevant to disease and cancer. Here, a sensitive in vitro assay for accurate analysis of RNA methylation writer activity on synthetic or in vitro transcribed RNAs is provided. This assay uses a tritiated form of S-adenosyl-methionine, resulting in direct labeling of methylated RNA with tritium. The low energy of tritium radiation makes the method safe, and pre-existing methods of tritium signal amplification, make it possible to quantify and to visualize the methylated RNA without the use of antibodies, which are commonly prone to artifacts. While this method is written for RNA methylation, few tweaks make it applicable to the study of other RNA modifications that can be radioactively labeled, such as RNA acetylation with 14C acetyl coenzyme A. Overall, this assay allows to quickly assess RNA methylation conditions, inhibition with small molecule inhibitors, or the effect of RNA or enzyme mutants, and provides a powerful tool to validate and expand results obtained in cells.

Here, an antibody-free in vitro assay for direct analysis of methyltransferase activity on synthetic or in vitro transcribed RNA is described.

There are more than 100 chemically distinct modifications of RNA, two thirds of which consist of methylations. Interest in RNA modifications, and ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...