BIOINFORMATICS APPLICATIONS NOTE
Vol. 26 no. 23 2010, pages 2979–2980
SmashCell: a software framework for the analysis of single-cell
amplified genome sequences
Eoghan D. Harrington1,2,∗, Manimozhiyan Arumugam3, Jeroen Raes4, Peer Bork3,5and
David A. Relman1,2,6
1Department of Microbiology and Immunology,2Department of Medicine, Stanford University School of Medicine,
Stanford, CA 94305, USA,3EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany,4VIB - Vrije Universiteit Brussel,
Pleinlaan 2, 1050 Brussels, Belgium,5Max Delbrück Center for Molecular Medicine, D-13092 Berlin, Germany and
6Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
Associate Editor: Dmitrij Frishman
Advance Access publication October 21, 2010
Summary: Recent advances in single-cell manipulation technology,
whole genome amplification and high-throughput sequencing have
now made it possible to sequence the genome of an individual cell.
The bioinformatic analysis of these genomes, however, is far more
complicated than the analysis of those generated using traditional,
culture-based methods. In order to simplify this analysis, we have
developed SmashCell (Simple Metagenomics Analysis SHell-for
sequences from single Cells). It is designed to automate the main
steps in microbial genome analysis—assembly, gene prediction,
functional annotation—in a way that allows parameter and algorithm
exploration at each step in the process. It also manages the data
created by these analyses and provides visualization methods for
rapid analysis of the results.
Availability: The SmashCell source code and a comprehensive
manual are available at http://asiago.stanford.edu/SmashCell
Supplementary information: Supplementary data are available at
Received on May 14, 2010; revised on September 10, 2010;
accepted on September 30, 2010
The rapid evolution of DNA sequencing platforms has had a
dramatic, beneficial impact on the study of microbial ecology
and population genetics. So far, these benefits have mostly come
from shotgun community metagenomics that provides a high-
level overview of the taxonomic and functional composition of
microbial communities [see Arumugam et al. (2010) for details].
However, this approach is limited in its ability to yield complete
genome sequences as well as the fine-scale genetic variation that
defines population substructures within these communities. One
possible solution uses a combination of single-cell manipulation
throughput sequencing to generate single-cell amplified genomes
(SAGs). This approach has already been used to characterize the
genomes of uncultivated microbes (Marcy et al., 2007; Woyke
et al., 2009) and as the throughput of the associated technologies
∗To whom correspondence should be addressed.
increase it should become possible to obtain high-resolution profiles
of populations or communities.
However, it is more difficult to produce a high-quality assembly
and functional annotation from a SAG than from the output of
and sequencing (detailed below). To overcome this requires an
iterative, exploratory approach that transforms the traditional linear
process of genome assembly, gene prediction and functional
annotation into a tree-like structure, each branch defined by a
different choice of algorithm or parameters, one of which will be
chosen as the final version (Fig. 1A). This approach is not easily
achieved using existing tools, which take an assembled genome as
a comparison with existing tools see SupplementaryTable). In order
to automate the process and deal with the resulting combinatorial
on SAGs many of its analyses are equally applicable to traditional
microbial genome sequences and low-complexity metagenomes.
SmashCell automates the steps common to most genome analyses—
some of the challenges posed by SAGs. For instance, it is difficult
to isolate a single cell for sequencing without including some
environmental DNA, in effect creating a metagenome. As a result,
SmashCell includes both sequence similarity and k-mer based
tools to identify potential contaminants, the latter being especially
useful when the target genome and/or contaminants are not closely
related to existing genome sequences (see Fig. 1B for details).
Another challenge with SAGs is the orders of magnitude variance in
MDA product abundance along the genome, which creates several
obstacles to obtaining high-quality annotation. First, it hampers
genome assembly, as most algorithms are designed for lower and
more evenly distributed read depth. Secondly, it vastly increases
the amount of sequencing required to obtain a complete genome
sequence. To address the first challenge, SmashCell includes scripts
to downsample overrepresented regions of the SAG and to address
the second, SmashCell uses the STRING database (Jensen et al.,
2009) to obtain counts of single-copy orthologous groups (Fig. 1C),
which can then be used to estimate genome completeness. In
Published by Oxford University Press on behalf of the US Government 2010.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
E.D.Harrington et al.
Fig. 1. (A) The data model used in SmashCell is designed to reduce redundancy and facilitate the comparison of results using different parameters and/or
algorithms [MC: metagenome collection, MG: metagenome (equivalent to a SAG), AS: assembly, GP: gene prediction, FUNC: functional annotation]. (B)
K-mer frequency statistics supplement sequence similarity information to identify potential contaminants. This shows a self-organizing map (SOM) trained
on the tetramer frequencies of an assembly. The left panel shows a series of pie charts highlighting the taxonomic identity (determined by best hit in GenBank,
those with no hits are uncoloured) of the contigs assigned to each neuron. The right panel shows the U-matrix of the SOM. (C) The abundance of single-copy
COGs can be used to assess genome completeness, the presence of contamination and the quality of the assembly. (D) SmashCell uses different graphs to aid
in parameter and algorithm selection. Here the results from two different gene prediction algorithms are presented, along with GC-content, quality scores and
visualization and other tools (Fig. 1D) that are generally applicable
to genomic and metagenomic data. SmashCell uses the same basic
data model as SmashCommunity [designed for shotgun community
sequencing; Arumugam et al. (2010)]. As a result, several of the
analyses available in SmashCell can be run on data generated by
SmashCommunity and vice versa. Documentation for these and
many more features are available on the SmashCell website.
SmashCell is a framework written in Python that provides a variety
of analysis tools that can be used either from the command line or
from within other Python scripts. The main function of SmashCell
is to automate the common steps in genome analysis in a way
that facilitates parameter and algorithm exploration. Using the data
model shown in Figure 1A, SmashCell manages the files and
data associated with each of these steps, reducing redundancy and
providing a layer of abstraction that simplifies access to these
data. SmashCell also uses generic databases to provide a common
format for assembly and gene prediction information, allowing it to
work with a variety of third-party assemblers and gene prediction
algorithms. In order to facilitate the exploration of genomic data,
SmashCell automatically generates many different types of graphs
DESIGN AND IMPLEMENTATION
We would like to thank S. Pamp and P. Blainey for assistance with
Funding: Human Frontiers Science Program (LTF to E.D.H);
National Institutes of Health (1R01HG004863 and Director’s
Pioneer Award to D.A.R); Thomas C. and Joan M. Merigan
Community FP7 [MetaHIT].
Conflict of Interest: none declared.
Arumugam,M. et al. (2010) SmashCommunity: a metagenomic annotation and analysis
Jensen,L.J. et al. (2009) STRING 8–a global view on proteins and their functional
interactions in 630 organisms. Nucleic Acids Res., 37, D412–D416.
Marcy,Y. et al. (2007) Dissecting biological “dark matter” with single-cell genetic
analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl
Acad. Sci. USA, 104, 11889–11894.
Woyke,T. et al. (2009) Assembling the marine metagenome, one cell at a time. PloS
ONE, 4, e5299.