Genus-Wide Comparative Genomics of Malassezia Delineates Its Phylogeny, Physiology, and Niche Adaptation on Human Skin.

Malassezia is a unique lipophilic genus in class Malasseziomycetes in Ustilaginomycotina, (Basidiomycota, fungi) that otherwise consists almost exclusively of plant pathogens. Malassezia are typically isolated from warm-blooded animals, are dominant members of the human skin mycobiome and are associated with common skin disorders. To characterize the genetic basis of the unique phenotypes of Malassezia spp., we sequenced the genomes of all 14 accepted species and used comparative genomics against a broad panel of fungal genomes to comprehensively identify distinct features that define the Malassezia gene repertoire: gene gain and loss; selection signatures; and lineage-specific gene family expansions. Our analysis revealed key gene gain events (64) with a single gene conserved across all Malassezia but absent in all other sequenced Basidiomycota. These likely horizontally transferred genes provide intriguing gain-of-function events and prime candidates to explain the emergence of Malassezia. A larger set of genes (741) were lost, with enrichment for glycosyl hydrolases and carbohydrate metabolism, concordant with adaptation to skin's carbohydrate-deficient environment. Gene family analysis revealed extensive turnover and underlined the importance of secretory lipases, phospholipases, aspartyl proteases, and other peptidases. Combining genomic analysis with a re-evaluation of culture characteristics, we establish the likely lipid-dependence of all Malassezia. Our phylogenetic analysis sheds new light on the relationship between Malassezia and other members of Ustilaginomycotina, as well as phylogenetic lineages within the genus. Overall, our study provides a unique genomic resource for understanding Malassezia niche-specificity and potential virulence, as well as their abundance and distribution in the environment and on human skin.

Charged residues next to transmembrane regions revisited: "Positive-inside rule" is complemented by the "negative inside depletion/outside enrichment rule".

BACKGROUND: Transmembrane helices (TMHs) frequently occur amongst protein architectures as means for proteins to attach to or embed into biological membranes. Physical constraints such as the membrane's hydrophobicity and electrostatic potential apply uniform requirements to TMHs and their flanking regions; consequently, they are mirrored in their sequence patterns (in addition to TMHs being a span of generally hydrophobic residues) on top of variations enforced by the specific protein's biological functions. RESULTS: With statistics derived from a large body of protein sequences, we demonstrate that, in addition to the positive charge preference at the cytoplasmic inside (positive-inside rule), negatively charged residues preferentially occur or are even enriched at the non-cytoplasmic flank or, at least, they are suppressed at the cytoplasmic flank (negative-not-inside/negative-outside (NNI/NO) rule). As negative residues are generally rare within or near TMHs, the statistical significance is sensitive with regard to details of TMH alignment and residue frequency normalisation and also to dataset size; therefore, this trend was obscured in previous work. We observe variations amongst taxa as well as for organelles along the secretory pathway. The effect is most pronounced for TMHs from single-pass transmembrane (bitopic) proteins compared to those with multiple TMHs (polytopic proteins) and especially for the class of simple TMHs that evolved for the sole role as membrane anchors. CONCLUSIONS: The charged-residue flank bias is only one of the TMH sequence features with a role in the anchorage mechanisms, others apparently being the leucine intra-helix propensity skew towards the cytoplasmic side, tryptophan flanking as well as the cysteine and tyrosine inside preference. These observations will stimulate new prediction methods for TMHs and protein topology from a sequence as well as new engineering designs for artificial membrane proteins.

MicroRNA expression profiling of human blood monocyte subsets highlights functional differences.

Within human blood there are two subsets of monocytes that can be identified by differential expression of CD16. Although numerous phenotypic and functional differences between the subsets have been described, little is known of the mechanisms underlying the distinctive properties of the two subsets. MicroRNAs (miRNAs) are small non-coding RNAs that can regulate gene expression through promoting mRNA degradation or repressing translation, leading to alterations in cellular processes. Their potential influence on the functions of monocyte subsets has not been investigated. In this study, we employed microarray analysis to define the miRNA expression profile of human monocyte subsets. We identified 66 miRNAs that were differentially expressed (DE) between CD16(+) and CD16(-) monocytes. Gene ontology analysis revealed that the predicted targets of the DE miRNAs were predominantly associated with cell death and cellular movement. We validated the functional impacts of selected DE miRNAs in CD16(-) monocytes, over-expression of miR-432 significantly increases apoptosis, and inhibiting miR-19a significantly reduces cell motility. Furthermore, we found that miR-345, another DE miRNA directly targets the transcription factor RelA in monocytes, which resulted in the differential expression of RelA in monocyte subsets. This implicates miR-345 indirect regulation of many genes downstream of RelA, including important inflammatory mediators. Together, our data show that DE miRNAs could contribute substantially to regulating the functions of human blood monocytes.

On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation.

BACKGROUND: Protein sequence similarities to any types of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc. where either positional sequence conservation is the result of a very simple, physically induced pattern or rather integral sequence properties are critical) are pertinent sources for mistaken homologies. Regretfully, these considerations regularly escape attention in large-scale annotation studies since, often, there is no substitute to manual handling of these cases. Quantitative criteria are required to suppress events of function annotation transfer as a result of false homology assignments. RESULTS: The sequence homology concept is based on the similarity comparison between the structural elements, the basic building blocks for conferring the overall fold of a protein. We propose to dissect the total similarity score into fold-critical and other, remaining contributions and suggest that, for a valid homology statement, the fold-relevant score contribution should at least be significant on its own. As part of the article, we provide the DissectHMMER software program for dissecting HMMER2/3 scores into segment-specific contributions. We show that DissectHMMER reproduces HMMER2/3 scores with sufficient accuracy and that it is useful in automated decisions about homology for instructive sequence examples. To generalize the dissection concept for cases without 3D structural information, we find that a dissection based on alignment quality is an appropriate surrogate. The approach was applied to a large-scale study of SMART and PFAM domains in the space of seed sequences and in the space of UniProt/SwissProt. CONCLUSIONS: Sequence similarity core dissection with regard to fold-critical and other contributions systematically suppresses false hits and, additionally, recovers previously obscured homology relationships such as the one between aquaporins and formate/nitrite transporters that, so far, was only supported by structure comparison.

Mesenchymal transition and dissemination of cancer cells is driven by myeloid-derived suppressor cells infiltrating the primary tumor.

In order to metastasize, cancer cells need to acquire a motile phenotype. Previously, development of this phenotype was thought to rely on the acquisition of selected, random mutations and thus would occur late in cancer progression. However, recent studies show that cancer cells disseminate early, implying the existence of a different, faster route to the metastatic motile phenotype. Using a spontaneous murine model of melanoma, we show that a subset of bone marrow-derived immune cells (myeloid-derived suppressor cells or MDSC) preferentially infiltrates the primary tumor and actively promotes cancer cell dissemination by inducing epithelial-mesenchymal transition (EMT). CXCL5 is the main chemokine attracting MDSC to the primary tumor. In vitro assay using purified MDSC showed that TGF-ß, EGF, and HGF signaling pathways are all used by MDSC to induce EMT in cancer cells. These findings explain how cancer cells acquire a motile phenotype so early and provide a mechanistic explanation for the long recognized link between inflammation and cancer progression.

Transmembrane helix: simple or complex.

Transmembrane helical segments (TMs) can be classified into two groups of so-called 'simple' and 'complex' TMs. Whereas the first group represents mere hydrophobic anchors with an overrepresentation of aliphatic hydrophobic residues that are likely attributed to convergent evolution in many cases, the complex ones embody ancestral information and tend to have structural and functional roles beyond just membrane immersion. Hence, the sequence homology concept is not applicable on simple TMs. In practice, these simple TMs can attract statistically significant but evolutionarily unrelated hits during similarity searches (whether through BLAST- or HMM-based approaches). This is especially problematic for membrane proteins that contain both globular segments and TMs. As such, we have developed the transmembrane helix: simple or complex (TMSOC) webserver for the identification of simple and complex TMs. By masking simple TM segments in seed sequences prior to sequence similarity searches, the false-discovery rate decreases without sacrificing sensitivity. Therefore, TMSOC is a novel and necessary sequence analytic tool for both the experimentalists and the computational biology community working on membrane proteins. It is freely accessible at http://tmsoc.bii.a-star.edu.sg or available for download.

Fluorodeschloroketamine found as a street drug in drug seizures and drug driving cases in Hong Kong.

BACKGROUND: With the decline of the use of ketamine, one of the common drugs of abuse in Hong Kong, detection of ketamine-related analogues in local laboratories has been encountered. AIM: A brief account of the occurrence of fluorodeschloroketamine (FDCK) in forensic cases is reported through a retrospective study of all drug seizures and driving under the influence of drugs (DUID) cases since its first appearance. METHODS: Identification of FDCK in drug seizures was achieved through gas chromatography - mass spectrometry (GC-MS) and/or liquid chromatography - diode array detection (LC-DAD) methods while its quantification was performed using gas chromatography - flame ionization detection (GC-FID). For the analysis of blood samples in DUID cases, identification and quantification were performed using LC-MS/MS by monitoring the respective transitions of FDCK and fluorodeschloronorketamine (FDCNK) using ketamine-d4 and norketamine-d4 respectively as internal standards. RESULTS: Since its first submission in November 2018, a total of 74 drug seizure cases (151 items) and 6 drug driving cases were encountered till December 2019. Drug seizures found with FDCK were physically similar to those of ketamine seizures. The majority of items were detected with FDCK only (103 items, ∼67%) or as a mixture of FDCK with ketamine (42 items, ∼28%). The drug purity detected with either FDCK only or FDCK mixed with ketamine was high which was similar to those purity found in ketamine seizures. The blood drug concentrations of FDCK of the 6 drug driving cases were in the range of <0.002-1.1 µg/mL and other psychoactive drug(s)/metabolite(s) were also identified. Except for one case where the analysis of the metabolite, fluorodeschloronorketamine (FDCNK), was not conducted due to insufficient sample, the FDCK (FDCNK) concentrations in blood found in the 6 cases were <0.002 (0.005), 0.002 (0.002), 0.002 (0.003), 0.02 (0.035), 0.87 (0.44) and 1.1 (not determined) µg/mL. CONCLUSIONS: With the drug seizures found with FDCK resembled in physical appearance with ketamine seizures, users might likely misuse it as ketamine. Though complicated by other drugs found, it is speculated that the two cases with higher concentration of FDCK found in blood (1.1 and 0.87 µg/mL) might have contributed to the impairment observed.

Macrophages in human colorectal cancer are pro-inflammatory and prime T cells towards an anti-tumour type-1 inflammatory response.

High macrophage infiltration into tumours often correlates with poor prognoses; in colorectal, stomach and skin cancers, however, the opposite is observed but the mechanisms behind this phenomenon remain unclear. Here, we sought to understand how tumour-associated macrophages (TAMs) in colorectal cancer execute tumour-suppressive roles. We found that TAMs in a colorectal cancer model were pro-inflammatory and inhibited the proliferation of tumour cells. TAMs also produced chemokines that attract T cells, stimulated proliferation of allogeneic T cells and activated type-1 T cells associated with anti-tumour immune responses. Using colorectal tumour tissues, we verified that TAMs in vivo were indeed pro-inflammatory. Furthermore, the number of tumour-infiltrating T cells correlated with the number of TAMs, suggesting that TAMs could attract T cells; and indeed, type-1 T cells were present in the tumour tissues. Patient clinical data suggested that TAMs exerted tumour-suppressive effects with the help of T cells. Hence, the tumour-suppressive mechanisms of TAMs in colorectal cancer involve the inhibition of tumour cell proliferation alongside the production of pro-inflammatory cytokines, chemokines and promoting type-1 T-cell responses. These new findings would contribute to the development of future cancer immunotherapies based on enhancing the tumour-suppressive properties of TAMs to boost anti-tumour immune responses.

Gene expression profiling reveals the defining features of the classical, intermediate, and nonclassical human monocyte subsets.

New official nomenclature subdivides human monocytes into 3 subsets: the classical (CD14(++)CD16(-)), intermediate (CD14(++)CD16(+)), and nonclassical (CD14(+)CD16(++)) monocytes. This introduces new challenges, as monocyte heterogeneity is mostly understood based on 2 subsets, the CD16(-) and CD16(+) monocytes. Here, we comprehensively defined the 3 circulating human monocyte subsets using microarray, flow cytometry, and cytokine production analysis. We find that intermediate monocytes expressed a large majority (87%) of genes and surface proteins at levels between classical and nonclassical monocytes. This establishes their intermediary nature at the molecular level. We unveil the close relationship between the intermediate and nonclassic monocytes, along with features that separate them. Intermediate monocytes expressed highest levels of major histocompatibility complex class II, GFRα2 and CLEC10A, whereas nonclassic monocytes were distinguished by cytoskeleton rearrangement genes, inflammatory cytokine production, and CD294 and Siglec10 surface expression. In addition, we identify new features for classic monocytes, including AP-1 transcription factor genes, CLEC4D and IL-13Rα1 surface expression. We also find circumstantial evidence supporting the developmental relationship between the 3 subsets, including gradual changes in maturation genes and surface markers. By comprehensively defining the 3 monocyte subsets during healthy conditions, we facilitate target identification and detailed analyses of aberrations that may occur to monocyte subsets during diseases.

Arxes: retrotransposed genes required for adipogenesis.

Retrotransposed sequences arise from messenger RNAs (mRNAs) that have been reinserted into genomic DNA by reverse transcription. Usually, these sequences are embedded in dormant regions, collect missense mutations over time and constitute processed, nonfunctional pseudogenes. There are thousands of processed pseudogenes in the mouse and human genome. Here, we report evidence for two paralog genes (termed Arxes1 and Arxes2), which arose by retrotransposition of the signal peptidase Spcs3 followed by a segmental duplication event. They gained a functional promoter that we show to be transactivated by adipogenic transcription factors. We further show that the Arxes mRNAs are highly expressed in adipose tissue and strongly upregulated during adipogenesis in different cell models. Additionally, their expression is elevated by an anti-diabetic agent in vitro and in vivo. Importantly, we provide evidence that the Arxes genes are translated and that the proteins are located in the endoplasmic reticulum. Although the sequence similarity and subcellular location are reminiscent of their parental gene, our data suggest that the Arxes have developed a different function, since their expression is required for adipogenesis, whereas Spcs3 is dispensable. In summary, we report retrotransposed-duplicated genes that evolved from a parental gene to function in a tissue and adipogenesis-specific context.

More than 1,001 problems with protein domain databases: transmembrane regions, signal peptides and the issue of sequence homology.

Large-scale genome sequencing gained general importance for life science because functional annotation of otherwise experimentally uncharacterized sequences is made possible by the theory of biomolecular sequence homology. Historically, the paradigm of similarity of protein sequences implying common structure, function and ancestry was generalized based on studies of globular domains. Having the same fold imposes strict conditions over the packing in the hydrophobic core requiring similarity of hydrophobic patterns. The implications of sequence similarity among non-globular protein segments have not been studied to the same extent; nevertheless, homology considerations are silently extended for them. This appears especially detrimental in the case of transmembrane helices (TMs) and signal peptides (SPs) where sequence similarity is necessarily a consequence of physical requirements rather than common ancestry. Thus, matching of SPs/TMs creates the illusion of matching hydrophobic cores. Therefore, inclusion of SPs/TMs into domain models can give rise to wrong annotations. More than 1001 domains among the 10,340 models of Pfam release 23 and 18 domains of SMART version 6 (out of 809) contain SP/TM regions. As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins. More worryingly, we show explicit examples that the scores of clearly false-positive hits, even in global-mode searches, can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74 using conservative criteria, we find that at least between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. Thus, false-positive domain hits enforced by SP/TM regions can lead to dramatic annotation errors where the hit has nothing in common with the problematic domain model except the SP/TM region itself. We suggest a workflow of flagging problematic hits arising from SP/TM-containing models for critical reconsideration by annotation users.

Reconstruction of gene association network reveals a transmembrane protein required for adipogenesis and targeted by PPARγ.

We have developed a method for reconstructing gene association networks and have applied this method to gene profiles from 3T3-L1 cells. Priorization of the candidate genes pinpointed a transcript annotated as APMAP (adipocyte plasma membrane-associated protein). Functional studies showed that APMAP is upregulated in murine and human adipogenic cell models as well as in a genetic mouse model of obesity. Silencing APMAP in 3T3-L1 cells strongly impaired the differentiation into adipocytes. Moreover, APMAP expression was strongly induced by the PPARγ ligand rosiglitazone in adipocytes in vitro and in vivo in adipose tissue. Using ChIP-qPCR and luciferase reporter assays, we show a functional PPARγ binding site. In addition, we provide evidence that the extracellular C-terminal domain of APMAP is required for the function of APMAP in adipocyte differentiation. Finally, we demonstrate that APMAP translocates from the endoplasmatic reticulum to the plasma membrane during adipocyte differentiation.

ANNIE: integrated de novo protein sequence annotation.

Function prediction of proteins with computational sequence analysis requires the use of dozens of prediction tools with a bewildering range of input and output formats. Each of these tools focuses on a narrow aspect and researchers are having difficulty obtaining an integrated picture. ANNIE is the result of years of close interaction between computational biologists and computer scientists and automates an essential part of this sequence analytic process. It brings together over 20 function prediction algorithms that have proven sufficiently reliable and indispensable in daily sequence analytic work and are meant to give scientists a quick overview of possible functional assignments of sequence segments in the query proteins. The results are displayed in an integrated manner using an innovative AJAX-based sequence viewer. ANNIE is available online at: http://annie.bii.a-star.edu.sg. This website is free and open to all users and there is no login requirement.

Forensic drug analysis of chloro-N,N-dimethylcathinone (CDC) and chloroethcathinone (CEC): Identification of 4-CDC and 4-CEC in drug seizures and differentiation from their ring-substituted positional isomers.

We report our findings for the determination of 4-chloro-N,N-dimethylcathinone (4-CDC), a newly encountered NPS in drug seizures examined in our laboratory, and its differentiation from 4-chloroethcathinone (4-CEC), one of the most common cathinones examined locally, and their respective regioisomers, namely 2-CDC and 3-CDC, as well as 2-CEC and 3-CEC in routine drug analysis. As CDCs and CECs have the same molecular mass of 211 with similar and non-characteristic spectra when analysed by gas chromatography-electron ionization-mass spectrometer (GC-EI-MS), it is imperative to establish methods easily amendable for forensic laboratories to differentiate these substances unambiguously. To confirm the identity of the solid, reference standards of all regioisomers of CDC (i.e., 2-CDC, 3-CDC and 4-CDC) and CEC (i.e., 2-CEC, 3-CEC and 4-CEC) were acquired and analysed using GC-EI-MS, liquid chromatography-diode array detector (LC-DAD) and Fourier Transform Infrared Spectrophotometer (FTIR) commonly used in routine forensic drug analysis. In addition, drug analysis with gas chromatography-chemical ionization-mass spectrometer (GC-CI-MS) using methane as the reagent gas operated in positive mode was also explored. It is found that using GC-EI-MS, all isomers of CDCs and CECs eluted with close but different retention times. However, the mass spectra between respective regioisomers were similar and difficult to distinguish. Using LC-DAD, the retention times of all studied cathinones were again different although there were partial overlap between 3-CDC and 4-CDC as well as between 3-CEC and 4-CEC but they all have distinguishable UV spectra. Apart from the detection of quasimolecular ion as the most prominent ion for each cathinone, GC-CI-MS is considered a superior technique to determine all six cathinones where each cathinone showed a unique fragmentation pattern for ease of identification. The analytical techniques have been applied for the examination of drug seizures where 4-CDC and 4-CEC were unambiguously identified either as a single component or mixed components in the seized materials. While FTIR is capable of providing confirmative structural information for the cathinones with each regioisomer exhibits a distinctive pattern but requiring high drug purity, LC-DAD and GC-CI-MS are demonstrated to be useful techniques that can readily differentiate structurally similar synthetic cathinones (even in mixtures) for routine forensic drug analysis.

Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer.

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data's analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today's level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive.

Function of a membrane-embedded domain evolutionarily multiplied in the GPI lipid anchor pathway proteins PIG-B, PIG-M, PIG-U, PIG-W, PIG-V, and PIG-Z.

Distant homology relationships among proteins with many transmembrane regions (TMs) are difficult to detect as they are clouded by the TMs' hydrophobic compositional bias and mutational divergence in connecting loops. In the case of several GPI lipid anchor biosynthesis pathway components, the hidden evolutionary signal can be revealed with dissectHMMER, a sequence similarity search tool focusing on fold-critical, high complexity sequence segments. We find that a sequence module with 10 TMs in PIG-W, described as acyl transferase, is homologous to PIG-U, a transamidase subunit without characterized molecular function, and to mannosyltransferases PIG-B, PIG-M, PIG-V and PIG-Z. We conclude that this new, membrane-embedded domain named BindGPILA functions as the unit for recognizing, binding and stabilizing the GPI lipid anchor in a modification-competent form as this appears the only functional aspect shared among all proteins. Thus, PIG-U's likely molecular function is shuttling/presenting the anchor in a productive conformation to the transamidase complex.

Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis.

BACKGROUND: Though earlier works on modelling transcript abundance from vertebrates to lower eukaroytes have specifically singled out the Zip's law, the observed distributions often deviate from a single power-law slope. In hindsight, while power-laws of critical phenomena are derived asymptotically under the conditions of infinite observations, real world observations are finite where the finite-size effects will set in to force a power-law distribution into an exponential decay and consequently, manifests as a curvature (i.e., varying exponent values) in a log-log plot. If transcript abundance is truly power-law distributed, the varying exponent signifies changing mathematical moments (e.g., mean, variance) and creates heteroskedasticity which compromises statistical rigor in analysis. The impact of this deviation from the asymptotic power-law on sequencing count data has never truly been examined and quantified. RESULTS: The anecdotal description of transcript abundance being almost Zipf's law-like distributed can be conceptualized as the imperfect mathematical rendition of the Pareto power-law distribution when subjected to the finite-size effects in the real world; This is regardless of the advancement in sequencing technology since sampling is finite in practice. Our conceptualization agrees well with our empirical analysis of two modern day NGS (Next-generation sequencing) datasets: an in-house generated dilution miRNA study of two gastric cancer cell lines (NUGC3 and AGS) and a publicly available spike-in miRNA data; Firstly, the finite-size effects causes the deviations of sequencing count data from Zipf's law and issues of reproducibility in sequencing experiments. Secondly, it manifests as heteroskedasticity among experimental replicates to bring about statistical woes. Surprisingly, a straightforward power-law correction that restores the distribution distortion to a single exponent value can dramatically reduce data heteroskedasticity to invoke an instant increase in signal-to-noise ratio by 50% and the statistical/detection sensitivity by as high as 30% regardless of the downstream mapping and normalization methods. Most importantly, the power-law correction improves concordance in significant calls among different normalization methods of a data series averagely by 22%. When presented with a higher sequence depth (4 times difference), the improvement in concordance is asymmetrical (32% for the higher sequencing depth instance versus 13% for the lower instance) and demonstrates that the simple power-law correction can increase significant detection with higher sequencing depths. Finally, the correction dramatically enhances the statistical conclusions and eludes the metastasis potential of the NUGC3 cell line against AGS of our dilution analysis. CONCLUSIONS: The finite-size effects due to undersampling generally plagues transcript count data with reproducibility issues but can be minimized through a simple power-law correction of the count distribution. This distribution correction has direct implication on the biological interpretation of the study and the rigor of the scientific findings. REVIEWERS: This article was reviewed by Oliviero Carugo, Thomas Dandekar and Sandor Pongor.

xHMMER3x2: Utilizing HMMER3's speed and HMMER2's sensitivity and specificity in the glocal alignment mode for improved large-scale protein domain annotation.

BACKGROUND: While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocal-mode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. In addition, the incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis. RESULTS: In this work, both the speed of HMMER3 and glocal-mode alignment of HMMER2 are combined within the xHMMER3x2 framework for tackling the large-scale domain annotation task. Briefly, HMMER3 is utilized for initial domain detection so that HMMER2 can subsequently perform the glocal-mode, sequence-to-full-domain alignments for the detected HMMER3 hits. An E-value calibration procedure is required to ensure that the search space by HMMER2 is sufficiently replicated by HMMER3. We find that the latter is straightforwardly possible for ~80% of the models in the Pfam domain library (release 29). However in the case of the remaining ~20% of HMMER3 domain models, the respective HMMER2 counterparts are more sensitive. Thus, HMMER3 searches alone are insufficient to ensure sensitivity and a HMMER2-based search needs to be initiated. When tested on the set of UniProt human sequences, xHMMER3x2 can be configured to be between 7× and 201× faster than HMMER2, but with descending domain detection sensitivity from 99.8 to 95.7% with respect to HMMER2 alone; HMMER3's sensitivity was 95.7%. At extremes, xHMMER3x2 is either the slow glocal-mode HMMER2 or the fast HMMER3 with glocal-mode. Finally, the E-values to false-positive rates (FPR) mapping by xHMMER3x2 allows E-values of different model builds to be compared, so that any annotation discrepancies in a large-scale annotation exercise can be flagged for further examination by dissectHMMER. CONCLUSION: The xHMMER3x2 workflow allows large-scale domain annotation speed to be drastically improved over HMMER2 without compromising for domain-detection with regard to sensitivity and sequence-to-domain alignment incompleteness. The xHMMER3x2 code and its webserver (for Pfam release 27, 28 and 29) are freely available at http://xhmmer3x2.bii.a-star.edu.sg/ . REVIEWERS: Reviewed by Thomas Dandekar, L. Aravind, Oliviero Carugo and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section.

The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.

As biomolecular sequencing is becoming the main technique in life sciences, functional interpretation of sequences in terms of biomolecular mechanisms with in silico approaches is getting increasingly significant. Function prediction tools are most powerful for protein-coding sequences; yet, the concepts and technologies used for this purpose are not well reflected in bioinformatics textbooks. Notably, protein sequences typically consist of globular domains and non-globular segments. The two types of regions require cardinally different approaches for function prediction. Whereas the former are classic targets for homology-inspired function transfer based on remnant, yet statistically significant sequence similarity to other, characterized sequences, the latter type of regions are characterized by compositional bias or simple, repetitive patterns and require lexical analysis and/or empirical sequence pattern-function correlations. The recipe for function prediction recommends first to find all types of non-globular segments and, then, to subject the remaining query sequence to sequence similarity searches. We provide an updated description of the ANNOTATOR software environment as an advanced example of a software platform that facilitates protein sequence-based function prediction.