Deep sequencing of metagenomic datasets by next-generation technology, has revealed a richness and diversity of microbial species that far exceeded previous expectations.

In 2006 Mitchell Sogin presented the concept of a rare biosphere, low-abundance microbial populations that have persisted over large evolutionary time scales. Sogin described it as a ‘potentially inexhaustible reservoir of genomic innovation’ which lets microbial communities recover from environmental assault and allows them to adapt to changed circumstances.

How do scientists determine microbial diversity? Sequence reads covering the hypervariable regions of 16S rRNA genes are often used for classification. These ‘pyrotags’ (named thus since they are obtained on Roche’s 454 sequencer) can be classified by matching them against a rRNA sequence database.

The challenge is that for most microbial species the 16S rRNA gene is not known. An alternative is shotgun sequencing and grouping of reads into operational taxonomic units; i.e. a cluster of sequences with a defined difference to a neighboring cluster — commonly a sequence difference of 3% is required at the species level, 5% at the genus level.

But with predictions of an ever larger rare biosphere voices of caution are starting to be heard saying that its size is overestimated. Is everything that is classified as a new and rare species indeed real, or simply a sequencing error?

Most recently we featured an error correction program in our own pages that addressed this issue; and a note of caution by Phil Hugenholtz and colleagues about ‘Wrinkles in the rare biosphere’ just appeared in “Early View” in Environmental Microbiology.

How well founded are the concerns of an artificially inflated rare biosphere? Are current estimates of the rare biosphere really 10-fold too high? If so, what are the consequences — does it mean that the rare biosphere is far less important than assumed? Is it not such an inexhaustible reservoir after all but just background noise?

We understand that these are very controversial issues and we would love to hear from you.

Two Correspondences made the list of top downloads for July coming in at #3 and #4, demonstrating that while this format may not report new methods it does have information of high interest to readers. The two top downloads seem to highlight a high level of interest in assaying single cells and using FRET to examine protein dynamics.

Top 8 research papers published in the July issue

1. Quantitative analysis of gene expression in a single cell by qPCR

2. Mapping the structure and conformational movements of proteins with transition metal ion FRET

3. Limitations and possibilities of small RNA digital gene expression profiling

4. Enabling IMAC purification of low abundance recombinant proteins from E. coli lysates

5. In vivo fluorescence imaging with high-resolution microlenses

6. Protein interaction platforms: visualization of interacting proteins in yeast

7. Agouti C57BL/6N embryonic stem cells for mouse genetic resources

8. Reaching the protein folding speed limit with large, sub-microsecond pressure jumps

There has been very little movement in the ten most popular papers published prior to our July issue and downloaded during July. The exception is the appearance of the HUPO test sample paper from June appearing at #7. The papers below this have changed but mostly because a large number of papers have very similar download numbers and they shuffle around from month to month.

Top 10 research papers published prior to the July issue

1. Mapping and quantifying mammalian transcriptomes by RNA-Seq

2. mRNA-Seq whole-transcriptome analysis of a single cell

3. Universal sample preparation method for proteome analysis

4. Stem cell transcriptome profiling via massive-scale mRNA sequencing

5. Isolation of human iPS cells using EOS lentiviral vectors to select for pluripotency

6. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

7. A HUPO test sample study reveals common problems in mass spectrometry–based proteomics

8. Generation of transgene-free induced pluripotent mouse stem cells by the piggyBac transposon

9. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes

10. A versatile tool for conditional gene expression and knockdown