In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually.
We developed a novel text mining methodology based on a new concept of position weight matrices (PWMs) for text representation and feature generation. We applied PWMs in conjunction with the document-term matrix to extract with high accuracy associations between methylated genes and diseases from free text. The performance results are based on large manually-classified data. Additionally, we developed a web-tool, DEMGD, which automates extraction of these associations from free text. DEMGD presents the extracted associations in summary tables and full reports in addition to evidence tagging of text with respect to genes, diseases and methylation words. The methodology we developed in this study can be applied to similar association extraction problems from free text.
The new methodology developed in this study allows for efficient identification of associations between concepts. Our method applied to methylated genes in different diseases is implemented as a Web-tool, DEMGD, which is freely available at http://www.cbrc.kaust.edu.sa/demgd/. The data is available for online browsing and download.
Technological improvements have resulted in increased discovery of new microRNAs (miRNAs) and refinement and enrichment of existing miRNA families. miRNA families are important because they suggest a common sequence or structure configuration in sets of genes that hint to a shared function. Exploratory tools to enhance investigation of characteristics of miRNA families and the functions of family-specific miRNA genes are lacking. We have developed, miRNAVISA, a user-friendly web-based tool that allows customized interrogation and comparisons of miRNA families for hypotheses generation, and comparison of per-species chromosomal distribution of miRNA genes in different families. This study illustrates hypothesis generation using miRNAVISA in seven species. Our results unveil a subclass of miRNAs that may be regulated by genomic imprinting, and also suggest that some miRNA families may be species-specific, as well as chromosome- and/or strand-specific.
A fundamental problem in bioinformatics is genome assembly. Next-generation sequencing (NGS) technologies produce large volumes of fragmented genome reads, which require large amounts of memory to assemble the complete genome efficiently. With recent improvements in DNA sequencing technologies, it is expected that the memory footprint required for the assembly process will increase dramatically and will emerge as a limiting factor in processing widely available NGS-generated reads. In this report, we compare current memory-efficient techniques for genome assembly with respect to quality, memory consumption and execution time. Our experiments prove that it is possible to generate draft assemblies of reasonable quality on conventional multi-purpose computers with very limited available memory by choosing suitable assembly methods. Our study reveals the minimum memory requirements for different assembly programs even when data volume exceeds memory capacity by orders of magnitude. By combining existing methodologies, we propose two general assembly strategies that can improve short-read assembly approaches and result in reduction of the memory footprint. Finally, we discuss the possibility of utilizing cloud infrastructures for genome assembly and we comment on some findings regarding suitable computational resources for assembly.
Mutations in any genome may lead to phenotype characteristics that determine ability of an individual to cope with adaptation to environmental challenges. In studies of human biology, among the most interesting ones are phenotype characteristics that determine responses to drug treatments, response to infections, or predisposition to specific inherited diseases. Most of the research in this field has been focused on the studies of mutation effects on the final gene products, peptides, and their alterations. Considerably less attention was given to the mutations that may affect regulatory mechanism(s) of gene expression, although these may also affect the phenotype characteristics. In this study we make a pilot analysis of mutations observed in the regulatory regions of 24,667 human RefSeq genes. Our study reveals that out of eight studied mutation types, “insertions” are the only one that in a statistically significant manner alters predicted transcription factor binding sites (TFBSs). We also find that 25 families of TFBSs have been altered by mutations in a statistically significant manner in the promoter regions we considered. Moreover, we find that the related transcription factors are, for example, prominent in processes related to intracellular signaling; cell fate; morphogenesis of organs and epithelium; development of urogenital system, epithelium, and tube; neuron fate commitment. Our study highlights the significance of studying mutations within the genes regulatory regions and opens way for further detailed investigations on this topic, particularly on the downstream affected pathways.
SNP; insertion; deletion; mutation; transcription factor; transcription factor binding site; promoter region; bioinformatics
Bacterial degradation of steroid compounds is of high ecological and biotechnological relevance. Pseudomonas sp. strain Chol1 is a model organism for studying the degradation of the steroid compound cholate. Its draft genome sequence is presented and reveals one gene cluster responsible for the metabolism of steroid compounds.
Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org.
Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.
Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.
Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.
We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ∼30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.
email@example.com or firstname.lastname@example.org
Supplementary data are available at Bioinformatics online.
Sickle cell disease (SCD) is a fatal monogenic disorder with no effective cure and thus high rates of morbidity and sequelae. Efforts toward discovery of disease modifying drugs and curative strategies can be augmented by leveraging the plethora of information contained in available biomedical literature. To facilitate research in this direction we have developed a resource, Dragon Exploration System for Sickle Cell Disease (DESSCD) (http://cbrc.kaust.edu.sa/desscd/) that aims to promote the easy exploration of SCD-related data.
The Dragon Exploration System (DES), developed based on text mining and complemented by data mining, processed 419,612 MEDLINE abstracts retrieved from a PubMed query using SCD-related keywords. The processed SCD-related data has been made available via the DESSCD web query interface that enables: a/information retrieval using specified concepts, keywords and phrases, and b/the generation of inferred association networks and hypotheses. The usefulness of the system is demonstrated by: a/reproducing a known scientific fact, the “Sickle_Cell_Anemia–Hydroxyurea” association, and b/generating novel and plausible “Sickle_Cell_Anemia–Hydroxyfasudil” hypothesis. A PCT patent (PCT/US12/55042) has been filed for the latter drug repurposing for SCD treatment.
We developed the DESSCD resource dedicated to exploration of text-mined and data-mined information about SCD. No similar SCD-related resource exists. Thus, we anticipate that DESSCD will serve as a valuable tool for physicians and researchers interested in SCD.
Natural products are considered a rich source of new chemical structures that may lead to the therapeutic agents in all major disease areas. About 50% of the drugs introduced in the market in the last 20 years were natural products/derivatives or natural products mimics, which clearly shows the influence of natural products in drug discovery.
In an effort to further support the research in this field, we have developed an integrative knowledge base on Marine Sponge Compounds Interactions (Dragon Exploration System on Marine Sponge Compounds Interactions - DESMSCI) as a web resource. This knowledge base provides information about the associations of the sponge compounds with different biological concepts such as human genes or proteins, diseases, as well as pathways, based on the literature information available in PubMed and information deposited in several other databases. As such, DESMSCI is aimed as a research support resource for problems on the utilization of marine sponge compounds. DESMSCI allows visualization of relationships between different chemical compounds and biological concepts through textual and tabular views, graphs and relational networks. In addition, DESMSCI has built in hypotheses discovery module that generates potentially new/interesting associations among different biomedical concepts. We also present a case study derived from the hypotheses generated by DESMSCI which provides a possible novel mode of action for variolins in Alzheimer’s disease.
DESMSCI is the first publicly available (http://www.cbrc.kaust.edu.sa/desmsci) comprehensive resource where users can explore information, compiled by text- and data-mining approaches, on biological and chemical data related to sponge compounds.
Sponge compounds interactions; Natural products; Text-mining; Information integration; Knowledge base
Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias.
We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source.
Summary: In higher eukaryotes, the identification of translation initiation
sites (TISs) has been focused on finding these signals in cDNA or mRNA sequences. Using
Arabidopsis thaliana (A.t.) information, we developed
a prediction tool for signals within genomic sequences of plants that correspond to TISs.
Our tool requires only genome sequence, not expressed sequences. Its
sensitivity/specificity is for A.t. (90.75%/92.2%), for
Vitis vinifera (66.8%/94.4%) and for Populus
trichocarpa (81.6%/94.4%), which suggests that our tool can be
used in annotation of different plant genomes. We provide a list of features used in our
model. Further study of these features may improve our understanding of mechanisms of the
Availability and implementation: Our tool is implemented as an artificial
neural network. It is available as a web-based tool and, together with the source code,
the list of features, and data used for model development, is accessible at http://cbrc.kaust.edu.sa/dts.
Supplementary information: Supplementary data are available at Bioinformatics
Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC.
Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen.
To the best of our knowledge our study is the first to use a cancer disease model as the framework to identify hormone responsive genes. Although we used ESCC as the disease model and estrogen as the hormone, the methodology can be extended analogously to other diseases as the model and other hormones. We believe that our results provide useful information for those interested in genes responsive to hormones and in the design of hormone-based therapies.
Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference.
Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the naïve Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions.
Availability: The new models are provided as open source programs at http://sfb.kaust.edu.sa/Pages/Software.aspx.
Supplementary data are available at Bioinformatics Online.
Cone snails produce a distinctive repertoire of venom peptides that are used both as a defense mechanism and also to facilitate the immobilization and digestion of prey. These peptides target a wide variety of voltage- and ligand-gated ion channels, which make them an invaluable resource for studying the properties of these ion channels in normal and diseased states, as well as being a collection of compounds of potential pharmacological use in their own right. Examples include the United States Food and Drug Administration (FDA) approved pharmaceutical drug, Ziconotide (Prialt®; Elan Pharmaceuticals, Inc.) that is the synthetic equivalent of the naturally occurring ω-conotoxin MVIIA, whilst several other conotoxins are currently being used as standard research tools and screened as potential therapeutic drugs in pre-clinical or clinical trials. These developments highlight the importance of driving conotoxin-related research. A PubMed query from 1 January 2007 to 31 August 2011 combined with hand-curation of the retrieved articles allowed for the collation of 98 recently identified conotoxins with therapeutic potential which are selectively discussed in this review. Protein sequence similarity analysis tentatively assigned uncharacterized conotoxins to predicted functional classes. Furthermore, conotoxin therapeutic potential for neurodegenerative disorders (NDD) was also inferred.
Conus; cone snail; peptide; neuropeptide; conotoxin; nicotinic acetylcholine receptor; sodium channel; calcium channel; potassium channel
Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis.
We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality.
CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.
We present the draft genome of Haloplasma contractile, isolated from a deep-sea brine and representing a new order between Firmicutesand Mollicutes. Its complex morphology with contractile protrusions might be strongly influenced by the presence of seven MreB/Mbl homologs, which appears to be the highest copy number ever reported.
We present the draft genome of Halorhabdus tiamatea, the first member of the Archaeaever isolated from a deep-sea anoxic brine. Genome comparison with Halorhabdus utahensisrevealed some striking differences, including a marked increase in genes associated with transmembrane transport and putative genes for a trehalose synthase and a lactate dehydrogenase.
We present the genome of Salinisphaera shabanensis, isolated from a brine-seawater interface and representing a new order within the Gammaproteobacteria. Its adaptations to physicochemical and nutrient availability fluctuations include six genes encoding heavy metal-translocating P-type ATPases and multiple genes involved in iron uptake, siderophore production, and poly-β-hydroxybutyrate synthesis.
The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs.
Motivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms. In this work, we present one such poly(A) motif prediction method based on properties of human genomic DNA sequence surrounding a poly(A) motif. These properties include thermodynamic, physico-chemical and statistical characteristics. For predictions, we developed Artificial Neural Network and Random Forest models. These models are trained to recognize 12 most common poly(A) motifs in human DNA. Our predictors are available as a free web-based tool accessible at http://cbrc.kaust.edu.sa/dps. Compared with other reported predictors, our models achieve higher sensitivity and specificity and furthermore provide a consistent level of accuracy for 12 poly(A) motif variants.
Supplementary information: Supplementary data are available at Bioinformatics online.
MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.
We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network's behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.
We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance our understanding of the molecular mechanisms underlying human cancer development and OC in particular.
Our study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.
The results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.
We introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.
The barnacle Balanus amphitrite is a globally distributed biofouler and a model species in intertidal ecology and larval settlement studies. However, a lack of genomic information has hindered the comprehensive elucidation of the molecular mechanisms coordinating its larval settlement. The pyrosequencing-based transcriptomic approach is thought to be useful to identify key molecular changes during larval settlement.
Methodology and Principal Findings
Using 454 pyrosequencing, we collected totally 630,845 reads including 215,308 from the larval stages and 415,537 from the adults; 23,451 contigs were generated while 77,785 remained as singletons. We annotated 31,720 of the 92,322 predicted open reading frames, which matched hits in the NCBI NR database, and identified 7,954 putative genes that were differentially expressed between the larval and adult stages. Of these, several genes were further characterized with quantitative real-time PCR and in situ hybridization, revealing some key findings: 1) vitellogenin was uniquely expressed in late nauplius stage, suggesting it may be an energy source for the subsequent non-feeding cyprid stage; 2) the locations of mannose receptors suggested they may be involved in the sensory system of cyprids; 3) 20 kDa-cement protein homologues were expressed in the cyprid cement gland and probably function during attachment; and 4) receptor tyrosine kinases were expressed higher in cyprid stage and may be involved in signal perception during larval settlement.
Our results provide not only the basis of several new hypotheses about gene functions during larval settlement, but also the availability of this large transcriptome dataset in B. amphitrite for further exploration of larval settlement and developmental pathways in this important marine species.
Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation.
We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems.
The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.
Despite intense efforts to develop non-cytotoxic anticancer treatments, effective agents are still not available. Therefore, novel apoptosis-inducing drug leads that may be developed into effective targeted cancer therapies are of interest to the cancer research community. Targeted cancer therapies affect specific aberrant apoptotic pathways that characterize different cancer types and, for this reason, it is a more desirable type of therapy than chemotherapy or radiotherapy, as it is less harmful to normal cells. In this regard, marine sponge derived metabolites that induce apoptosis continue to be a promising source of new drug leads for cancer treatments. A PubMed query from 01/01/2005 to 31/01/2011 combined with hand-curation of the retrieved articles allowed for the identification of 39 recently confirmed apoptosis-inducing anticancer lead compounds isolated from the marine sponge that are selectively discussed in this review.
marine sponge; apoptosis; cancer treatment; targeted cancer therapy; anticancer