11. Additional software tools and resources for use in conjunction with Sequedex

In this chapter, we introduce the user to a variety of software tools and packages and how we have found them to be useful in the process of organizing data files and understanding the output. This is neither intended to be an exhaustive list of software packages nor a tutorial in using any of them; it is simply a short sketch of how we used widely available software to better understand the output of Sequedex.

Indeed, if the user has difficulties with the tools below, it is possible to choose from a wide array of open source analysis tools, such as those that have been described on Wikipedia and on seqanswer to identify an alternate method of acheiving the a similar result.

11.1. Graphing and sorting with Excel

Spreadsheets are popular tools for visualizing and comparing tabular outputs such as the phylogenetic and functional profiles produced by Sequedex. Excel is widely used on Windows and Mac operating systems, and can be run on Linux under the Windows Emulator (WINE, http://www.winehq.org/). WINE is also useful for running other Windows applications under Linux, such as BioEdit and reference alignments, but the open source spreadsheet programs Gnumeric and LibreOffice will also serve this function. Numerous books are available for Excel, and Gnumeric and LibreOffice both have extensive online documentation, here and here.

If you open (or ‘data -> import`) the Output files - Who?, Output files - What?, or Output files - Who does what? or Output files - stats files, it will be possible to see which categories are most-populated, to compare the different types of k-mer matches for the who file, and extract columns from different samples for direct comparison and statistical analysis. It is also possible to use the ‘consolidate’ function to perform Phylogenetic rollups and Functional rollups, although the user may want to further bundle the rollup categories or sort and discard them to simplify the plots. We supply programs, below, that perform the rollups shown in Phylogenetic rollups, which combines a number of multiphyla nodes into a single category.

Alternatively, the user can save output from multiple samples in a tab delimited text file to be compared with data from the Sequedex documentation or previously analyzed samples. When doing this, care must be taken to compare compatible samples, where the rows have the same meaning, and to ensure the user is able to provide column labels which adequately document which sample and analysis the data are from. The tree name is automatically used as part of the who, what, and wdw files, and the functional classifications are included in the what and wdw files, to help keep track of what analysis was performed.

11.2. How to install Cygwin and what to include.

Cygwin is a software package that provides a Unix-like environment under a native Windows operating system. It is available from http://cygwin.org/. During installation, you will be asked to select from about 30 categories of programs to install. While it is simple to rerun the setup.exe script at any time, if you plan to run a variety of bioinformatics tools it may be easiest to install Devel, Editors, Interpreters, Perl, and Python, since you often need to re-compile software developed for Linux from source code. Installing everything will require over 1 GB of disk space and typically require several hours of download time.

Once installed, Cygwin provides many of the functions of a command line with standard tools, discussed in the next section, including running the Sequedex executable, as described in the Running Sequedex from the command line section, although the s.install.cyg are distinct from eithr Linux or Windows without Cygwin. Note the the GUI version of Sequedex can be initiated by typing ‘sequescan’ on the command line of Cygwin, once Java and the path variables are correctly set.

Many programs, for example R, Python, and Matlab, can be installed and used either from within Cygwin or as a native Windows application in an integrated development environment (IDE). Typically the IDEs have numerous user-friendly features that you may find helpful. There is no difficult in mixing the different types of tools (native Windows and Cygwin), although navagation back and forth between the Windows home directory (My Documents) and the Cygwin home directory (typically c:/cygwin/home/username/) can be awkward. It is possible to put a shortcut (or link) to quickly navigate back and forth between these two frequented folders. Also, path names are treated differently in the two systems: see http://cygwin.com/cygwin-ug-net/using.html#using-pathnames.

11.3. The command line with standard tools (Linux, Mac, Cygwin for Windows)

The command line is not so much a software program as a flexible interface to the computer’s operating system. It enables a wide variety of software to be run on a variety of input files, placing the output in well defined places. As described in Directory structures for data, output files, and analysis, appropriate choices in where to place files for projects and samples can greatly facilitate the automation of analysis of large numbers of files. If scripts are used to perform the analysis, a record will be created of exactly what was done to each data set. For example, we have already described in Running Sequedex from the command line how to run Sequedex from the command line.

Two of the most popular ‘shells’ for the command line are bash and tcsh, and if the user is new to its use, he or she is encouraged to locate an introductory book and a colleague who is willing to help you from time to time. We will only describe the features needed to perform the Sequedex analysis tasks described in this manual.

  • The filesystems can be viewed and navigated with df -h, ls -lthr, and cd, for example, remembering that the ‘tab’ key normally auto-completes filenames or directory names.

  • Files (such as the Sequedex outputfiles, which are tab-delimited, can be viewed with cat, head, or grep Percent.

  • The functional categories can be sorted on the screen by decreasing prevalence with:

    sort -n -k3 -r what-Life2550-32GB.0xseed_0911.m1.tsv | less
  • The phylogenetic categories can be sorted on the screen by decreasing prevalence with:

    sort -n -k3 -r who-Life2550-32GB.0.tsv |less
  • A help page can be found for many commands by typing ‘man command’, such as ‘man less’ or ‘man awk’

  • Results from one command can be sent to another command with the pipe, or ‘|’, and to a file with ‘>’.

  • Commands can be iterated over a list with:

    for i in file1 file2 file3;do
      awk '{print $3}' < $i.fas.sqdx/what-Life2550-40GB.0xseed_0911.m1.tsv > $i.out ; done ; paste *.out > all.out
  • Commands can be saved in a file, and be run in batch by typing ‘source filename’. A short introduction is available here. Note that variables can be passed to the script from the command line.

  • The utilities ps2pdf, convert, and pdfunite are convenient tools for changing among image formats.

A simple text editor that is available in most terminal windows is emacs or xemacs, that can be used to edit the sequescan configuration file or modify any of the scripts and programs that are distributed with Sequedex. A short example script for creating a matrix of sequedex output from several runs is described in s.rollup. If the user knows how to program in Perl or Python already, it is easy to write short programs or scripts in these languages to process output files.

For numerical analysis tools, many options are available, including gfortran, which we used to do simple numerical operations on groups of profiles in Initial analysis with Sequedex: Phylogenetic and functional profiles, using pnorm.f90 or fnorm.f90. To use this code, you need to open the source code with a text editor (eg. emacs pnorm.f90 &), change the parameter in the 5th line to the appropriate number of parameters, and the input filename in the 9th line to the appropriate input filename, save the file, compile (gfortran pnorm.f90) and run the executable (./a.out). Several output files will be created, with normalized output, a rollup, and a similarity matrix of each sample compared to the others. The same objective could easily be accomplished in a variety of languages and analysis environments, such as Python, R, C, C++, Perl, or Matlab. It is a useful way to automate automate analysis and make use of functions written by others and distributed on the web.

11.4. Exploring trees with Archaeoptrix, FigTree, or NJPlot

Sequedex organizes its list of signature peptides with carefully constructed phylogenetic trees, ostensibly representing the ancestral history of the various organisms represented. Although Sequedex provides both html and pdf ways of looking at the trees in Tree of Life, 2550 taxa, specialized tree-viewing software will greatly facilitate the user’s ability to both understand the reference database and the results. The variety of tree-viewers and file formats for defining trees and associated meta-data include:

11.5. Comparing and annotating phylogenetic and functional profiles with Gnuplot

Gnuplot is rapid method to view output files, perhaps as Sequedex is running, as a script-able method to make and annotate a series of plots of phylogenetic or functional profiles.

The example script hi_res_HMB.gpt, makes and annotates a multi-panel plot of the total reads matched in each phylogenetic bin of the tol1471. It can be run by typing gnuplot hi_res_HMB.gpt with the appropriate input files (plaque, stool, tongue, cheek, nose, ear, and bds) in the path, and will produce the output postscript file ‘hmb.ps’. Much of the value of this method of plot-making lies in editing the example file (‘emacs hi_res_HMB.gpt &), and changing the output filename, perhaps producing a pdf or png file instead of postscript, changing the scaling factor from the data, or changing the input files or line colors.

The advantage of this tool is that it is lightweight and easy to modify. It it, however, specific to a particular tree or functional set (eg. seed_0911), as the elements are identified by their position in the output table, not the label.

11.6. Statistical analysis and graphing with R and R Studio

R (http://www.r-project.org/ is a powerful open source software for statistical data analysis. Binaries and download instruction are found at http://cran.r-project.org/. In addition to the basic installation, many users will want to peruse the library of contributed packages that extend the functionality and methods available. Packages to consider include ape, ade4, cluster, and rgl.

Although R is available within Cygwin, the user may find it more convenient to utilize the Windows binary distributions for R, enabling installation of Rstudio and utilization of Windows-specific distribution of packages.

Sequestat (described in detail in Using Sequestat) utilizes three add-on packages distributed for use within the R environment:

APE and GEE are used for displaying Sequedex output on the nodes of trees, while LARS is use by Sequestat for predicting which combination of reference genomes or metagenomes best-describes the phylogenetic profile of a metagenomic sample. Igraph is used for displaying functional profiles on a graph-layout of the hierarchical Seed subsystems. If the packages are downloaded, they can be installed from within R with the command:

install.packages(repos=NULL, pkgs="gee_4.13-18.tar.gz")

Packages and their dependencies can also be automatically downloades, but may require a proxy server be utilized, if your organization sits behind a firewall. This should already be set for sequedex-update and other network-using features of Sequedex, but can be done set from within R if needed by a comand such as:


Many users prefer to use an integrated development environment such as the freely distributed RStudio, from http://www.rstudio.com/ide/ instead of the command-line interface.

In either case, there are several basic functions the user needs to get started applying R to Sequedex data.

  • Getting to the correct directory: Within RStudio, there is a ‘set working directory option under ‘session’‘. R can just be started in the desired directory, or the command ‘setwd(“~/sample.fas.sqdx”)’.
  • Sourcing an external script so the functions and variables can be used interactively: ‘source(“~/sequestat.r”)’. In this case, the file sequestat4.r will be sourced, assuming it is in the R directory of user’s home directory, and the external package, ape, described above will be loaded.
  • Loading a nexus format phylogenetic tree, such as Life=Read.tree(“~/Life2550.nexus”), into the data structure, tol.
  • Reading tsv data into the data structure, dat: dat=read.table(“~/env.tsv”,sep=”t”). The first column needs to have node names, and the first row needs to have sample names. Both the rows and columns can be referred to by either element number (position) or label.
  • Attaching the tsv file just loaded to the tree structure: env$data=dat.
  • Combining two data matrixes: all$data=c(env$data,new$data). (Don’t forget to also update the column labels)
  • Changing plot output to a png file instead of the screen: png(“rplot.png”) ; plot(x,y) ; dev.off()

Although a high-throughput genomics analysis package for R Bioconductor has been written, we have not yet incorporated features from this toolkit into our workflows.

11.7. Obtaining reference data from NCBI

NCBI has several interfaces with which data can be downloaded. We downloaded bacterial and archael genomes from the NCBI ftp site under the link for ‘Genome annotation and assembly projects’ for ‘Bacteria` and Bacteria_DRAFT. Subdirectories for each of the genomes exist with various file-types and information for download. For the Bacteria, gzipped tar files exist to easily download a particular file-type for all of the organisms, such as amino acid sequences of called genes, in the faa file.

Data for eukaryotes was obtained from the ‘RefSeq’ link at the NCBI ftp site, and a fair amount of effort was required to identify and extract genes from each of the organisms.

11.8. Creating synthetic data

There are many reasons one might want to create synthetic data. In using this technique to evaluate analysis software, however, one needs to keep in mind that simulated data often derives directly from the same database of reference organisms that the analysis software uses to classify reads. Unfortunately, the reference genomes are not necessarily representative of observed natural diversity. For well-characterized pathogens, this problem is relatively minor. For most RNA viruses and environmental metagenomes, this problem can be quite severe.

MetaSim is a software package that can simulate metagenomic data from both reference genomes and evolutionary models that incorporates sequencer type-specific errors and distributions of read lengths. Since many metagenomics analysis methods, in particular those relying on an assembly step, are quite non-linear in that the results depend on both sequencing depth and the richness and diversity of the sequenced sample, such a flexible tool to generate synthetic data is probably essential to improving the method. For nucleotide-based methods of recognizing reads, such as Metaphlan and Phylophlan testing the analysis software with synthetic data derived from reference genomes is more problematic than with amino-acid-based recognition of reads, such as employed by Sequedex.

Beyond the need to characterize the impact of sequencing errors, database bias, and the impact of mixtures on analysis performance, it is also useful to characterize the phylogenetic dependence of the sensitivity and specificity of read-based annotation. For this purpose, synthetic data can be obtained by simply chopping the full nucleotide sequence of an organism into equal-length reads with, for example, the following two-line perl / bash script:

cat *.fna |grep -v ">"|tr -d '\n' |perl -ne 'while ($c = substr($_, 0, 100, "")) {print "$c\n"}' > syn
grep -n . syn |tr ':' ' '|awk '{print ">"$1"\n" $2}' > syn.fas;echo $i

11.9. Comparing functional profiles to KEGG

The Kyoto Encyclopedia of Genes and Genomes KEGG provides an extensive collection of how genes interact with one another in pathways. For highly conserved genes where paralogs performing a distinct function are absent, it is relatively straightforward to relate a gene fragment to a position in a KEGG pathway map. For most genes, however, this mapping can be error-prone, as described at in the SEED manifesto of the SEED project.

Consequently, we currently base our functional assignments on the Definition of functional classifications, and rely on further analysis of reads with reference genomes to confirm functional assignments, using the techniques described in Annotated reads and the reference data provided in Annotated reads to get the user started on this semi-infinite task. Anyone doubting the complexity of this task is invited to browse the Pfam families and assess the clarity of mapping onto the KEGG pathway maps.

11.10. The Ribosomal Database Project

In part because universally conserved regions permit PCR amplification ribosomal sequences, the database and tools for probing the microbial community structure are quite well-developed. The Ribosomal Database Project has systemetically collected and curated ribosomal sequence data, and developed and made available numerous tools for its analysis. Begining with the Life2550 tree, Sequedex has defined a functional category for ribosomal sequence, and, using the techniques described in Annotated reads, will generate a fasta or fastq file of all identified bacterial, archael, and eukaryotic ribosomal sequences, both the SSU and LSU.

11.11. Bergey’s manual

When specific organisms are identified in a sequencing data set, they can be indicative of the ecological niche the sample came from. In addition to the peer reviewed literature, the references to Genbank and annotated pdf files provided in Tree of Life, 2550 taxa, and several online wiki sites, Bergey’s manual of systematic microbiology proveds a systematic exposition of cultured bacterial and archaeal organisms.

11.12. Phylogeny vs. taxononomy - MEGAN and Krona plots

Several tools exist for visualizing phylogenetic output. Because Sequedex is defined by and utilizes a phylogenetic tree to appropriately capture abiguity in phylogenetic placement on a read-by-read basis, such as Graphing and sorting with Excel, Tree of Life, 2550 taxa, and Using Sequestat. Nevertheless, since phylogenetic rollups provide reasonably good correspondence to the NCBI taxonomy, and many users will be familliar with tools such as MEGAN and Krona to represent hierarchical placement of reads in a taxonomy, it may be worthwhile for the user to map the Sequedex output into these formats.

11.13. Removal of duplicate reads with CD-HIT

Software to remove duplicates from pyrosequencing data is available at http://weizhong-lab.ucsd.edu/cd-hit/.

11.14. BioPython, BioPerl, and EMBOSS utilities

In the flexible workflows described in the Sequedex documentation, especially regarding the phylogenetic analysis, the user will encounter numerous instances when sequences need to be translated, aligned, annotated, visualized on networks, or compared, or may want to bundle workflows together in a larger, more complex, program. While we typically provide robust examples of hot these tasks can be solved, several buldled packages of utilities are available, together with source code and documentation to enable modification. Examples of these packages include BioPython, BioPerl, and the EMBOSS utilities.

11.15. BioEdit and reference alignments

Multiple sequence alignments are a powerful tool for assessing the noise characteristics of sequencing errors and for placing novel sequences in the context of the existing database of genome sequences. Additionally, they provide the starting point for evolutionary analysis, such as that described in the section Tree building with phyML or FastTree. Such analysis was used to compute the reference tree for Sequedex’s signature calculation, and the reference alignments are provided in Phylogenetic placement of RNA Polymerase reads. Additionally, when a dominant species is found in a metagenome, it is often useful to compare the nucleotide sequence of a phylogenetic marker gene to that of an assembled unknown, such as provided in Nucleotide Alignments for Strain Attribution for E. coli and its neighbors.

If your web browser has a constant width font, the above-mentioned alignments will be rendered on your screen so that each column contains corresponding a corresponding amino acid or nucleotide. If the sequences are sufficiently similar to one another, numerous software packages (see Muscle, and HMMER and Pfam) are available to construct such alignments from appropriate unaligned sequence data. If, however, too much divergence is present in the input file or chimeric or inappropriate sequences are included in the input file, automated alignment programs can fail, sometimes quite badly. More divergent nucleotide sequences, such as from viral genomes, can often be more easily aligned as translated proteins, and then un-translated for the evolutionary analysis.

In this case, a sequence alignment viewer and editor can be quite useful. One of the most versatile is BioEdit, which is freely available, although it was written in Visual Basic, and will run only under a Windows operating system or Windows emulator, such as WINE.

When used for viewing alignments, sequencing errors, stray bar-codes, frame-shift mutations, chimeric reads or assemblies, mis-placed start codons, and un-alignable proteins are often glaringly obvious. Because many alignment programs place similar sequences next to one another in the alignment, it is also possible to distinguish orthologs from paralogs, and either select a sub-set of sequences for re-alignment, or delete problematic sequences and re-align the remainders.

Besides making it easier to spot inconsistencies and errors, sequence editors also enable manual editing of sequences (and sequence labels) in the alignment, by sliding residues or blocks of residues left or right, deleting residues judged to be errors, or, if the user so desires, sequences can be manually typed in or changed, hopefully with some justification. Although BioEdit does not provide a mechanism to access the quality scores, it is quite versitile, and numerous strategies exist to obtain alignments that are appropriate inputs to evolutionary analysis.

When downloading the reference alignments provided in the sections Phylogenetic placement of RNA Polymerase reads and Nucleotide Alignments for Strain Attribution, the user will need to change them into one of the supported file formats, such as fasta. It is a good practice to manually inspect all parts of any alignment which will be used for evolutionary analysis, perhaps consulting available structures and literature in cases where the sequence data alone is ambiguous.

For simply viewing alignments, ClustalX and SeaView both work well.

11.16. BLAST and nr / nt databases

Although much of the convenience of running Blast as a confirmatory analysis lies in the accessibility of the continuously updated databases and parallelized compuataional capacity availble through the web interface at NCBI, it is also quite valuable to create customized databases for use on local machines. Such databases can allow rapid searches for particular genes, over restricted phylogenetic search-space, or over a subset of organisms that are appropriately selected and annotated for the purposes of annotation.

A customized Blast database can be created from an unaligned fasta file with the command:

./makeblastdb -in RNAP.fas -dbtype ‘prot’ -out RNAP -name -RNAP

A Blast search can be run against a local database with the any of the commands:

blastn -db C:\blast\db\bact_complete.fna -query contigs_l.fa -out velveth_l.nt -num_alignments 1 blastn -db ~/megan/nt -query contigs_l.fa -out velveth_l.nt -num_threads 4 -num_alignments 1 blastn -db ~/megan/nt -query rnap.fa -out rnap.nt -num_threads 4 -num_alignments 1 -outfmt 7

The top one is appropriate for a Windows machine running Cygwin, while the bottom two work on a Linux machine with four processors, and produce two different types of output file formats. Blast is an extremely flexible software package with extensive documentation.

11.17. HMMER and Pfam

HMMER constructs a statistical representation of the conserved elements in a multiple sequence alignment (a Hidden Markov Model) that can be used to rapidly align and score unknows against the original alignment. It is useful for rapidly aligning and classifying contigs or fragments against a family of proteins that can either be created from a curated reference or downloaded from (Pfam http://pfam.janelia.org/ is an exhaustive collection of annotated protein families). For the highest confidence in the match, it is suggested that a tree be built with reference genes and the annotations checked. This is a way to distinguish orthologs from paralogs.

11.18. Muscle

Muscle http://www.drive5.com/muscle/ is rapid enough to simply re-align both reference and unknown sequences, although it is also possible to use it to add sequences to a frozen alignment.

11.19. Velvet and de-novo assembly

Velvet is a robust assembler that does not require quality scores. It can be run with the following two commands:

velveth 701 21 701.fa
velvetg 701

These commands will create an assembly of the reads in the file 701.fa with several output files in the directory 701. The 21 on the first line refers to the length of overlap identity required for two reads to be identified as overlapping. To sort the assembled contigs in order of decreasing length, from the directory created by velvet (701 in the above example), type:

tr '\n' ';' < contigs.fa |sed "s/>/\n>/g" |tr '_' ' '|sort -n -r -k4 |sed "s/[0-9];/\n/" |tr -d ';' > sort.contigs.fa

The longest contigs are likely to be the most useful in identifying novel organisms, although simply selecting relevant reads for alignment will sometimes provide reads of nearly the same length, but with much greater choice in location (very important for comparing across samples, were it is useful to have the same region selected from all samples). It is also possible to estimate coverage from the assembly, and compare to that expected from examination of phylogenetic profiles.

11.20. Tree building with phyML or FastTree

Once an unknown read or contig has been placed into a multiple sequence alignment, and the alignment has been examined and / or curated, it is possible to compute a phylogenetic tree. Although neighbor-joining trees, such as can be easily run from ClustalX or BioEdit, can be a quick-and-dirty of screening sequences for appropriateness for inclusion of sequences in an alignment, anything of importance should be run through a maximum likelihood tree-building algorithm, such as phyML. Although maximum likelihood methods can be time consuming (they typically scale linearly with alignment length and cubically with the number of sequences, a variety of methods and options are available, and it is generally possible to curate the list of sequences to less than 100 taxa (which aids interpretation as well), so there isn’t really a good reason for lengthy computations here.

11.21. p-values and Pplacer

Pplacer is designed to place metagenomic reads according to a reference alignment and tree, and enable downstream visualization and analysis. It requires a reference alignment, a potentially large number of reads aligned to this reference alignment, perhaps with HMMER and Pfam, and a tree-building algorithm, such as Tree building with phyML or FastTree.

11.22. BWA and BowTie2

When a suitable reference genome is within a couple of percent divergence in nucleotide sequence from an unknown in a metagenomic sample, read-mappers such BWA or BowTie2 can rapidly extract reads or contigs from large input files in a few minutes. Quality scores are preserved and a file format specially designed for such large alignments (SAM) as well as its binary version (BAM) are supported. A set of commands that creates such an alignment is:

bwa index all.na.fas
bwa aln -n 2 all.na.fas ../../../test008.fastq > aln.5.sai
bwa samse all.na.fas aln.sai ../../../test008.fastq > aln.sam

In the next section, Samtools and Bamtools, we will generate a consensus fasta file from the above.

11.23. Samtools and Bamtools

Samtools, Bamtools, and VCFtools are freely available utilities to perform a variety of useful manipulations on large alignments in the SAM or BAM files. VCF tools is an additional software suite associated with the Variant Call Format, designed to enable SNP analysis in the presence of sequencing errors. These files are best viewed with a specialized viewer, such as the Integrated Genomics Viewer, described in the next section.

The above example from BWA can be continued to create a consensus fasta file with the commands:

samtools faidx all.na.fas
samtools view -bt all.na.fas.fai -o aln.bam aln.sam
samtools sort aln.bam aln.sorted
samtools index aln.sorted.bam
samtools mpileup -uf all.na.fas rnap.bam | bcftools view -bvcg - > var.raw.bcf
bcftools view var.raw.bcf | vcfutils.pl varFilter -D100 > var.flt.vcf
samtools mpileup -uf all.na.fas rnap.sorted.bam | bcftools view -cg - | vcfutils.pl vcf2fq > cns.fq

The process of iteratively distinguishing sequence errors from true variants and correctly associating SNPs with one another is well-traveled territory; see, for example, vphaser.

11.24. The Integrated Genomics Viewer

The Integrated Genomics Viewer from the Broad Institute is designed to compare large, disparate, data sets that can be mapped onto a reference genome. It requires reference genomes and can accommodate annotations in the gff format, available with completed and draft genomes from NCBI, described in Obtaining reference data from NCBI. It is primarily used for resequencing of large, eukaryotic, genomes, so the user will likely need to create local reference files for particular genomes of interest if environmental or microbiome data are being analyzed.

Since one valuable function the IGV enables is evaluating genome inventory, it is important to consider both the quality of the annotation and the completeness of the genome inventory, in addition to phylogenetic distance when choosing a reference genome.