MagicLamp is a computational pipeline for targeted annotation of genomes using HMM sets.
Please cite MagicLamp if you use it in your publication:
Garber, AI., Merino, N., Ramirez, GA., Sadeghpour, S. Pavia MJ., McAllister, SM. (2020) MagicLamp: toolkit for annotation of genomic data using discreet and curated HMM sets. 2023: MagicLamp, GitHub repository: https://github.com/Arkadiy-Garber/MagicLamp.
Choose your genie, upload your genome files, and let the magic begin!
LithoGenie is a computational bioinformatics tool designed for the targeted detection and annotation of genes involved in lithotrophic metabolism, metal cycling, and carbon fixation. It leverages Hidden Markov Models (HMMs) to identify functional modules across diverse microbial genomes, providing insights into key biogeochemical processes.
Identifies genes such as Cyc2 and Cyc1, crucial for extracellular electron transfer in iron oxidation and reduction pathways.
Detects components like MtoA, MtrA, MtrC, and MnxG, which play roles in microbial iron oxidation and reduction.
Includes markers such as dsrA, dsrB, soxZ, and sulfide quinone oxidoreductase (sqr), essential for sulfur metabolism.
Screens for genes encoding key enzymes in the Calvin-Benson-Bassham (CBB) cycle (rubisco), the reductive tricarboxylic acid (rTCA) cycle (aclA, aclB), and the Wood-Ljungdahl pathway (codhC, codhD).
Recognizes hydrogenases (Hydrogenase_Group_1-4, FeFe_hydrogenase, NiFe_hydrogenase) that facilitate microbial energy generation via hydrogen oxidation.
Identifies various cytochrome oxidases (CytCoxidase_aa3_coxA/B, CytCoxidase_cbb3_ccoN/O/P, Cyt_bd_oxidase CydA/B), playing roles in aerobic and anaerobic respiration.
Annotates genes involved in methane and nitrogen cycling, including mcrA/B/G (methanogenesis), napA/B (nitrate reduction), and nirK/S (nitrite reduction).
Screens for arsenate reductases (arsC), selenium metabolism genes (selenate reductase ygfK/M), and detoxification enzymes.
FeGenie is a bioinformatics tool designed for the identification of iron-related genes and gene neighborhoods in genome and metagenome assemblies. It utilizes Hidden Markov Models (HMMs) to detect key iron metabolism functions in microbial communities.
Detects genes involved in heme oxygenation, including HemO, HmoB, HugZ, HupZ, IsdG, MhuD, and PigA.
Identifies transporters for heme uptake such as HasB, Heme_HasD, Heme_HasE, HmuV, HxuA/B, and PF06438.
Includes genes for iron uptake such as EfeB, FbpA, FbpB, FbpC, FutA1, and Iron_uptake_YfeA.
Detects genes like Cyc1, Cyc2, FoxA, MtrA, MtrB, MtrC, and OmcS, crucial for microbial iron redox cycling.
Identifies iron storage-related genes such as Ferritin and Transferrin.
Recognizes genes involved in magnetosome formation, including MamA, MamB, MamE, MamK, MamM, and MamO.
FeGenie provides an essential resource for studying microbial iron metabolism, aiding in the discovery of genetic mechanisms underpinning iron utilization across diverse environmental and host-associated ecosystems.
Lucifer is a bioinformatics tool designed to detect and analyze genes involved in microbial light perception, energy harvesting, and phototrophic metabolism. It identifies key photoreceptor and photosystem components that enable light-driven biological processes.
Detects light-sensitive ion pumps and receptors, including Bac_rhodopsin, Proteorhodopsin, Heliorhodopsin, and Xanthorhodopsin, which contribute to phototrophic energy conversion.
Identifies genes encoding core photosystem proteins such as Photo_RC, PsaA_PsaB (Photosystem I), PSII (Photosystem II), and PsbH, essential for capturing and utilizing light energy.
Recognizes genes like LuxAB, LuxC, LuxD, and LuxE, which encode luciferase enzymes responsible for bioluminescence in bacteria.
Detects photoreceptors and signal transduction components such as 7tm_1, ASRT, Htr2, and GpcrRhopsn4, which enable microbial responses to light.
Identifies proteins involved in capturing and transferring light energy, including BChl_A (bacteriochlorophyll), Bac_chlorC, Chloroa_b-bind (chlorophyll-binding proteins), and PCP (peridinin-chlorophyll proteins).
LightGenie enables researchers to explore microbial photobiology, from photosynthesis and bioluminescence to light-driven sensory responses, helping to uncover the diverse ways microbes utilize and respond to light.
RiboGenie is a bioinformatics tool designed to detect and analyze genes encoding bacterial ribosomal proteins, essential for translation, protein synthesis, and cellular growth. It identifies core components of the 30S and 50S ribosomal subunits, providing insights into ribosome biogenesis, function, and evolutionary adaptations.
Identifies genes encoding the small ribosomal subunit, including: rpsA (S1), rpsB (S2), rpsC (S3), rpsD (S4), rpsE (S5), rpsF (S6), rpsG (S7), rpsH (S8), rpsI (S9), rpsJ (S10), rpsK (S11), rpsL (S12), rpsM (S13), rpsN (S14), rpsO (S15), rpsP (S16), rpsQ (S17), rpsR (S18), rpsS (S19), rpsT (S20), and rpsU (S21).
Detects genes encoding the large ribosomal subunit, including: rplA (L1), rplB (L2), rplC (L3), rplD (L4), rplE (L5), rplF (L6), rplJ (L10), rplK (L11), rplL (L12), rplM (L13), rplN (L14), rplO (L15), rplP (L16), rplQ (L17), rplR (L18), rplS (L19), rplT (L20), rplU (L21), rplV (L22), rplW (L23), rplX (L24), rplY (L25), rplZ (L27), rplAA (L28), rplBB (L29), rplCC (L30), rplDD (L31), rplEE (L32), rplFF (L33), rplGG (L34), rplHH (L35), and rplII (L36).
Recognizes key ribosome assembly proteins that aid in the maturation and structural organization of the ribosome.
Identifies genes involved in ribosome-associated quality control, including ribosome recycling factors, elongation factors, and peptidyl transferase center components that regulate protein synthesis efficiency.
RiboGenie provides a framework for studying microbial translation mechanisms, aiding researchers in understanding protein synthesis, ribosomal evolution, and antibiotic targeting strategies.
PlasticGenie is a bioinformatics tool designed to detect and analyze microbial genes involved in **plastic degradation, polymer metabolism, and xenobiotic compound breakdown**. It identifies key enzymes and pathways enabling microbial degradation of synthetic polymers and related compounds.
Identifies enzymes such as **cutinases** (cutinase_CazyDB) and **polyethylene terephthalate hydrolases** (pet_hydrolase), which contribute to microbial plastic degradation.
Detects genes like **nylon hydrolases** (nylA, nylB, nylC), which enable bacteria to metabolize nylon oligomers.
Recognizes sulfonate-processing enzymes, including sfnG, ssuA, ssuB, ssuC, and ssuD, which play roles in breaking down sulfur-containing xenobiotics.
Identifies genes like UreA, UreB, and UreC, which aid in urea and amide degradation, supporting microbial adaptation to nitrogen-rich waste environments.
Detects manganese peroxidases (mnp), laccases (Cu_oxidase), and related lignin breakdown enzymes, crucial for degrading complex organic polymers.
Includes regulators such as asfR, tsaR, and dszR, which coordinate microbial adaptation to plastic-associated environments.
**PlasticGenie** enables researchers to explore microbial-driven plastic degradation, providing insights into bioremediation, synthetic polymer metabolism, and sustainable waste management.
GasGenie is a bioinformatics tool designed to detect and analyze genes responsible for gas vesicle formation—specialized protein-bound structures that provide buoyancy to aquatic microorganisms, allowing them to regulate their position in the water column.
Identifies essential structural genes such as gvpA, gvpB, and gvpC, which encode the protein framework of gas vesicles.
Detects genes like gvpD, gvpE, and gvpF, which play key roles in vesicle assembly and regulatory processes.
Recognizes accessory genes such as gvpG, gvpH, gvpI, and gvpK which contribute to vesicle stability and adaptation to environmental conditions.
Includes additional candidates such as gvpP, gvpQ, and gvpR, which may have unique functional roles in different microbial lineages.
GasGenie provides a powerful framework for studying microbial buoyancy mechanisms, enabling researchers to explore the ecological and evolutionary significance of gas vesicle formation across diverse microbial environments.
CircadianGenie is a bioinformatics tool designed to detect and analyze genes involved in **circadian rhythm regulation** in cyanobacteria. These genes control daily cycles of gene expression, metabolism, and photosynthetic activity in response to light-dark cycles.
Identifies the essential oscillatory components of the cyanobacterial clock, including **KaiA**, **KaiB**, and **KaiC**, which generate and maintain the daily biological rhythm.
Detects sensory and regulatory proteins such as **CikA**, a histidine kinase that integrates environmental cues via **CikA_HisKA**, **CikA_GAF**, and **CikA_HATPase_c** domains.
Recognizes **LdpA**, a component involved in **light and redox sensing**, linking environmental conditions to circadian control mechanisms.
Includes **KaiC_ATPase**, which functions as the central clock oscillator by undergoing phosphorylation cycles that regulate circadian feedback loops.
**CircadianGenie** provides a framework for studying microbial timekeeping mechanisms, offering insights into how cyanobacteria optimize photosynthetic activity and cellular metabolism in sync with daily environmental fluctuations.
WspGenie is a bioinformatics tool for detecting and analyzing the **Wsp biofilm formation operon** in microbial genomes. It focuses on key genetic components involved in cyclic-di-GMP signaling, biofilm formation, and surface sensing.
Identifies genes responsible for biofilm initiation, including wspA (MCP-like receptor), wspB, and wspC, which regulate the phosphorylation cascade controlling c-di-GMP levels.
Detects key regulatory genes such as wspD, wspE, and wspF, which activate WspR, the diguanylate cyclase responsible for c-di-GMP synthesis.
Recognizes genes like wspR, a major diguanylate cyclase, that enhances biofilm formation and cellular adhesion in response to surface contact.
Detects genes linked to EPS production and biofilm stability, integrating Wsp signaling with alginate synthesis pathways in select bacterial species.
WspGenie enables researchers to explore biofilm formation mechanisms and cyclic-di-GMP regulation, aiding in the study of microbial community structure and persistence in environmental and clinical settings.
MnGenie is a bioinformatics tool designed to detect and analyze genes involved in **manganese metabolism, oxidation-reduction processes, and oxidative stress defense**. Manganese plays a crucial role in cellular processes, including electron transport, antioxidant defense, and enzyme activation.
Identifies key transporters such as **mntA**, **mntA1**, **mntB**, and **mntH**, which regulate manganese uptake and intracellular homeostasis.
Detects genes like **MnxG**, **MtrA**, **MtrB**, and **MtrC**, which facilitate extracellular electron transfer and manganese oxidation.
Recognizes manganese-dependent superoxide dismutases (**SOD_FeMn_Cterminal**, **SOD_FeMn_Nterminal**) and peroxidases (**An_peroxidase**, **catalase_Glutathione_peroxidase**, **Mn_catalase**) that protect cells from reactive oxygen species.
Identifies genes such as **PsbY**, which plays a role in photosynthetic electron transport, and **PEPCK_ATP**, an enzyme involved in energy metabolism.
Includes genes like **Ribonuc_red_sm** (ribonucleotide reductase) and **McoA** (multicopper oxidase), which use manganese as a cofactor for enzymatic activity.
**MnGenie** provides a framework for studying microbial manganese metabolism, helping researchers understand its role in metal cycling, oxidative stress resistance, and biogeochemical processes.
ROSGenie is a bioinformatics tool designed to detect and analyze genes involved in **neutralizing reactive oxygen species (ROS)**—toxic byproducts of cellular metabolism that can cause oxidative stress and damage biomolecules.
Identifies key detoxification proteins such as **1-cysPeroxiredoxin** and **Alkyl-hydroperoxide reductase**, which play crucial roles in reducing peroxides and protecting cells from oxidative damage.
Detects major ROS-scavenging enzymes, including **Catalase**, **Mn-catalase**, and **Catalase-peroxidase**, which decompose hydrogen peroxide into water and oxygen.
Recognizes iron, manganese, and copper-zinc **superoxide dismutases (SODs)**, including **SOD_FeMn_C-terminal**, **SOD_FeMn_N-terminal**, and **Sod_CuZn**, which convert superoxide radicals into less harmful molecules.
Includes genes such as **catalase_Glutathione_peroxidase**, which contribute to glutathione-dependent pathways for detoxifying hydrogen peroxide and lipid peroxides.
Identifies genes like **CCP_MauG**, which encode cytochrome peroxidases involved in electron transfer and oxidative stress mitigation.
**ROSGenie** provides a framework for studying microbial oxidative stress responses, helping researchers understand how organisms survive in high-oxygen environments and protect themselves from ROS-induced damage.
PolymeraseGenie is a bioinformatics tool designed to detect and analyze genes involved in **DNA replication, repair, and polymerase activity**. These genes are critical for genome stability, proofreading, and nucleotide incorporation during replication.
Identifies essential polymerase enzymes, including **DNA_pol3_alpha** (the catalytic subunit of DNA polymerase III), **DNA_pol_A**, and **DNA_pol_B**, which play key roles in DNA replication and repair.
Detects proofreading domains such as **5_3_exonuc**, **DNA_pol_A_exo1**, and **DNA_pol_B_exo1**, which enhance replication fidelity by removing misincorporated nucleotides.
Recognizes additional repair-associated proteins such as **RNase_T**, which is involved in RNA primer removal, and **DUF4113**, a domain linked to nucleic acid metabolism.
Includes IMS-related proteins (**IMS**, **IMS_C**, **IMS_HHH**) that contribute to DNA binding, structure maintenance, and polymerase stability.
**PolymeraseGenie** provides a framework for studying microbial DNA replication mechanisms, enabling researchers to explore genome maintenance, polymerase evolution, and replication fidelity across diverse organisms.
MagnetoGenie is a bioinformatics tool designed to detect and analyze genes involved in **magnetosome formation**—the biomineralization of intracellular magnetic particles in magnetotactic bacteria. It identifies key genetic components that regulate magnetosome synthesis, organization, and function.
Identifies core genes such as MamB, MamM, MamL, and MamQ, essential for the formation of the magnetosome membrane vesicle.
Detects genes like MamA, MamO, and MamP, which regulate iron uptake and magnetite biomineralization.
Recognizes cytoskeletal proteins such as MamK and MamI, which are responsible for arranging magnetosomes into a linear chain for efficient magnetotaxis.
Includes key regulators like MamE, which plays a role in protein sorting and magnetosome maturation.
**MagnetoGenie** provides a framework for exploring the genetic mechanisms of magnetosome formation, offering insights into bacterial navigation, geomicrobiology, and potential applications in biotechnology and nanomaterials.