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2.  Correction: Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi 
BMC Genomics  2014;15:6.
Abstract
The version of this article published in BMC Genomics 2013, 14: 274, contains 9 unpublished genomes (Botryobasidium botryosum, Gymnopus luxurians, Hypholoma sublateritium, Jaapia argillacea, Hebeloma cylindrosporum, Conidiobolus coronatus, Laccaria amethystina, Paxillus involutus, and P. rubicundulus) downloaded from JGI website. In this correction, we removed these genomes after discussion with editors and data producers whom we should have contacted before downloading these genomes. Removing these data did not alter the principle results and conclusions of our original work. The relevant Figures 1, 2, 3, 4 and 6; and Table 1 have been revised. Additional files 1, 3, 4, and 5 were also revised. We would like to apologize for any confusion or inconvenience this may have caused.
Background
Fungi produce a variety of carbohydrate activity enzymes (CAZymes) for the degradation of plant polysaccharide materials to facilitate infection and/or gain nutrition. Identifying and comparing CAZymes from fungi with different nutritional modes or infection mechanisms may provide information for better understanding of their life styles and infection models. To date, over hundreds of fungal genomes are publicly available. However, a systematic comparative analysis of fungal CAZymes across the entire fungal kingdom has not been reported.
Results
In this study, we systemically identified glycoside hydrolases (GHs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), and glycosyltransferases (GTs) as well as carbohydrate-binding modules (CBMs) in the predicted proteomes of 94 representative fungi from Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota. Comparative analysis of these CAZymes that play major roles in plant polysaccharide degradation revealed that fungi exhibit tremendous diversity in the number and variety of CAZymes. Among them, some families of GHs and CEs are the most prevalent CAZymes that are distributed in all of the fungi analyzed. Importantly, cellulases of some GH families are present in fungi that are not known to have cellulose-degrading ability. In addition, our results also showed that in general, plant pathogenic fungi have the highest number of CAZymes. Biotrophic fungi tend to have fewer CAZymes than necrotrophic and hemibiotrophic fungi. Pathogens of dicots often contain more pectinases than fungi infecting monocots. Interestingly, besides yeasts, many saprophytic fungi that are highly active in degrading plant biomass contain fewer CAZymes than plant pathogenic fungi. Furthermore, analysis of the gene expression profile of the wheat scab fungus Fusarium graminearum revealed that most of the CAZyme genes related to cell wall degradation were up-regulated during plant infection. Phylogenetic analysis also revealed a complex history of lineage-specific expansions and attritions for the PL1 family.
Conclusions
Our study provides insights into the variety and expansion of fungal CAZyme classes and revealed the relationship of CAZyme size and diversity with their nutritional strategy and host specificity.
doi:10.1186/1471-2164-15-6
PMCID: PMC3893384  PMID: 24422981
Fungi; CAZymes; Glycoside hydrolase; Polysaccharide lyase; Carbohydrate esterase; Pectinase; Cutinase; Lignocellulase
6.  Correction: Unraveling overlapping deletions by agglomerative clustering 
BMC Genomics  2013;14(Suppl 1):S16.
doi:10.1186/1471-2164-14-S1-S16
PMCID: PMC3599823
8.  Identification and analysis of the germin-like gene family in soybean 
BMC Genomics  2011;12:16.
In line 12 of page 1, replace "GmGER 9" with "GmGER 15".
doi:10.1186/1471-2164-12-16
PMCID: PMC3023749
11.  Correction: High throughput approaches reveal splicing of primary microRNA transcripts and tissue specific expression of mature microRNAs in Vitis vinifera 
BMC Genomics  2010;11:109.
The version of this article published in BMC Genomics 2009, 10:558, contains data in Table 1 which are now known to be unreliable, and an illustration, in Figure 1, of unusual miRNA processing events predicted by these unreliable data. In this full-length correction, new data replace those found to be unreliable, leading to a more straightforward interpretation without altering the principle conclusions of the study. Table 1 and associated methods have been corrected, Figure 1 deleted, supplementary file 1 added, and modifications made to the sections "Deep sequencing of small RNAs from grapevine leaf tissue" and "Microarray analysis of miRNA expression". The editors and authors regret the inconvenience caused to readers by premature publication of the original paper.
Background
MicroRNAs are short (~21 base) single stranded RNAs that, in plants, are generally coded by specific genes and cleaved specifically from hairpin precursors. MicroRNAs are critical for the regulation of multiple developmental, stress related and other physiological processes in plants. The recent annotation of the genome of the grapevine (Vitis vinifera L.) allowed the identification of many putative conserved microRNA precursors, grouped into multiple gene families.
Results
Here we use oligonucleotide arrays to provide the first indication that many of these microRNAs show differential expression patterns between tissues and during the maturation of fruit in the grapevine. Furthermore we demonstrate that whole transcriptome sequencing and deep-sequencing of small RNA fractions can be used both to identify which microRNA precursors are expressed in different tissues and to estimate genomic coordinates and patterns of splicing and alternative splicing for many primary miRNA transcripts.
Conclusions
Our results show that many microRNAs are differentially expressed in different tissues and during fruit maturation in the grapevine. Furthermore, the demonstration that whole transcriptome sequencing can be used to identify candidate splicing events and approximate primary microRNA transcript coordinates represents a significant step towards the large-scale elucidation of mechanisms regulating the expression of microRNAs at the transcriptional and post-transcriptional levels.
doi:10.1186/1471-2164-11-109
PMCID: PMC2831844  PMID: 20152027
14.  The extracellular Leucine-Rich Repeat superfamily; a comparative survey and analysis of evolutionary relationships and expression patterns 
BMC Genomics  2009;10:230.
Correction to Dolan J, Walshe K, Alsbury S, Hokamp K, O'Keeffe S, Okafuji T, Miller SF, Tear G, Mitchell KJ: The extracellular leucine-rich repeat superfamily; a comparative survey and analysis of evolutionary relationships and expression patterns. BMC Genomics 2007, 8:320.
doi:10.1186/1471-2164-10-230
PMCID: PMC2689278
15.  Gene expression and isoform variation analysis using Affymetrix exon arrays 
BMC Genomics  2009;10:121.
Correction to Bemmo A, Benovoy D, Kwan T, Gaffney DJ, Jensen RV, Majewski J: Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC Genomics 2008, 9: 529.
doi:10.1186/1471-2164-10-121
PMCID: PMC2666767
22.  Discovery and validation of breast cancer subtypes 
BMC Genomics  2007;8:101.
Following the publication of our recent article (Kapp et al., BMC Genomics 2006, 7:231), we (the authors) regrettably found several errors in the published Table 5. This correction article not only describes what makes the published Table 5 incorrect, it also presents the correct Table 5.
doi:10.1186/1471-2164-8-101
PMCID: PMC1855057

Results 1-24 (24)